Mobility-aware mechanisms for fog node discovery and selection

The recent development of delay-sensitive applications has led to the emergence of the fog computing paradigm. Within this paradigm, computation nodes present at the edge of the network can act as fog nodes (FNs) capable of processing users' tasks, thus resulting in latency reductions compared to the existing cloud-based execution model. In order to realize the full potential of fog computing, new research questions have arised, mainly due to the dynamic and heterogeneous fog computing context. This thesis focuses on the following questions in particular: How can a user detect the presence of a nearby FN? How should a user on the move adapt its FN discovery strategy, according to its changing context? How should an FN be selected , in the case of user mobility and FN mobility? These questions will be addressed throughout the different contributions of this thesis. The first contribution consists in proposing a discovery solution allowing a user to become aware of the existence of a nearby FN. Using our solution, the FN advertizes its presence using custom WiFi beacons, which will be detected by the user via a scan process. An implementation of this approach has been developed and its evaluation results have shown that it results in a non-negligible energy consumption given its use of WiFi. This has led to our second contribution, which aims at improving the WiFi scan performed in our discovery approach, especially in the case of user mobility. At a first stage, this improvement consisted in embedding information about the topology of the FNs in the beacons the user receives from previous FNs. We have shown that by adapting the scan behavior based on this information, considerable energy savings can be achieved, while guaranteeing a high discovery rate. However, as this approach is associated with a restrictive FN topology structure, we proposed a different alternative, at a second stage. This alternative leverages the history of cellular context information as an indicator allowing the user to infer whether an FN may be present in its current location. If so, the scan will be enabled. Otherwise, it is disabled. The simulation results comparing different classification algorithms have shown that a sequence-based model, such as a hidden-Markov model is able to effectively predict the FN presence in the current user location. While the previous approaches have focused on a sparse FN deployment, our third contribution considers a high density of FNs. Consequently, as there are multiple nearby FNs that can process the user's tasks, it is important to derive a suitable FN selection strategy. This strategy should consider the time-varying set of FNs caused by the user's mobility. Besides, it should minimize the number of switches from one FN to another, in order to maintain a good quality of service. With these considerations in mind, we have shown that an adaptive greedy approach, that selects an FN having a good-enough delay estimate, achieves the best results. Finally, unlike the previous contribution, where the focus has been on FN selection when the user is mobile, our final contribution deals with mobile vehicular FNs (VFNs). Given the mobility of such VFNs, it is important to make the most of their resources, since they are only available for a short time at a given area. So, we propose that, in order to select an appropriate VFN for a given task, a reference roadside unit (RSU) responsible for task assignment can use advice from a neighbor RSU. This advice consists in the VFN that will result in the lowest delay for the current task, based on the experience of the neighbor RSU. The results have shown that, using the provided advice, the reference RSU can observe significant delay reductions. All in all, the proposed contributions have addressed various problems that may arise in a fog computing context and the obtained results can be used to guide the development of the building blocks of future fog computing solutions.El recent desenvolupament d'aplicacions IoT ha comportat l'aparició del paradigma de fog computing. Dins d'aquest paradigma, els nodes de càlcul presents a la vora de la xarxa poden actuar com a “fog nodes'' (FN) capaços de processar les tasques dels usuaris, produint així reduccions de latència en comparació amb el model d'execució basat en núvol. Per assolir tot el potencial del fog computing, han sorgit noves qüestions de recerca, principalment a causa del context dinàmic i heterogeni de fog computing. Aquesta tesi se centra especialment en les qüestions següents: Com pot un usuari detectar la presència d'un FN? Com hauria d’adaptar un usuari en moviment la seva estratègia de descobriment de FN, segons el seu context? Com s’ha de seleccionar un FN, en el cas de la mobilitat dels usuaris i la mobilitat FN? Aquestes preguntes s’abordaran al llarg de les diferents aportacions d’aquesta tesi. La primera contribució consisteix a proposar una solució de descobriment que permeti a l'usuari detectar l’existència d’un FN proper. Mitjançant la nostra solució, un FN anuncia la seva presència mitjançant beacons Wi-Fi personalitzats, que seran detectats per l'usuari mitjançant un procés d’exploració. S'ha desenvolupat una implementació d'aquest enfocament i els seus resultats d’avaluació han demostrat que resulta en un consum d'energia menyspreable donat el seu ús del Wi-Fi. Això ha suposat la nostra segona contribució, que té com a objectiu millorar l’exploració Wi-Fi, especialment en el cas de la mobilitat dels usuaris. En una primera fase, aquesta millora va consistir a incorporar informació sobre la topologia dels FN en les beacons que rep l'usuari dels FN anteriors. Hem demostrat que mitjançant l'adaptació del comportament d'escaneig basat en aquesta informació es pot aconseguir un estalvi considerable d’energia, alhora que es garanteix un índex elevat de descobriment. Tanmateix, com aquest enfocament s'associa a una estructura de topologia FN restrictiva, vam proposar una alternativa diferent, en una segona etapa. Aquesta alternativa aprofita la història de la informació del context cel·lular com a indicador que permet a l'usuari deduir si un FN pot estar present en la seva ubicació. En cas afirmatiu, l'exploració estarà habilitada. Els resultats de la simulació comparant diferents algoritmes de classificació han demostrat que un model basat en seqüències, com un model HMM, és capaç de predir eficaçment la presència de FNs a la ubicació actual de l'usuari. Si bé els enfocaments anteriors s’han centrat en un desplegament escàs de FNs, la nostra tercera contribució considera una alta densitat d'FNs. En conseqüència, com que hi ha múltiples FNs propers que poden processar les tasques de l'usuari, és important derivar una estratègia de selecció de FN adequada. Aquesta estratègia hauria de tenir en compte el conjunt variable de temps causat per la mobilitat de l'usuari. A més, hauria de minimitzar el nombre de canvis d'un FN a un altre, per mantenir una bona qualitat del servei. Tenint en compte aquestes consideracions, hem demostrat que un enfocament codiciós adaptatiu, que selecciona un FN amb una estimació de retard suficient, aconsegueix els millors resultats. Finalment, a diferència de l'aportació anterior, on l'atenció s'ha fixat en la selecció d'FN quan l'usuari és mòbil, la nostra contribució final tracta sobre les FNs per a vehicles mòbils (VFNs). Tenint en compte la mobilitat d’aquests VFNs, és important aprofitar al màxim els seus recursos, ja que només estan disponibles per a un temps curt. Així doncs, proposem que, per seleccionar un VFN adequat per a una tasca, una unitat RSU responsable de l'assignació de tasques pot utilitzar consells d'un RSU veí. Aquest consell consisteix en escollir el VFN que suposarà el menor retard de la tasca actual, en funció de l’experiència del RSU veí. Els resultats han demostrat que ..

A survey on mobility-induced service migration in the fog, edge, and related computing paradigms

The final publication is available at ACM via http://dx.doi.org/10.1145/3326540With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.Peer ReviewedPreprin

A user-centric mobility management scheme for high-density fog computing deployments

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe inherent mobility characterizing users in fog computing environments along with the limited wireless range of their serving fog nodes (FNs) drives the need for designing efficient mobility management (MM) mechanisms. This ensures that users' resource-intensive tasks are always served by the most suitable FNs in their vicinity. However, since MM decisionmaking requires control information which is difficult to predict accurately a-priori, such as the users' mobility patterns and the dynamics of the FNs, researchers have started to shift their attention towards MM solutions based on online learning. Motivated by this approach, in this paper, we consider a bandit learning model to address the mobility-induced FN selection problem, with a particular focus on scenarios with a high FN density. Following this approach, a software agent implemented within the user's device learns the FNs' delay performances via trial and error, by sending them the user's computation tasks and observing the perceived delay, with the goal of minimizing the accumulated delay. This task is particularly challenging when considering a high FN density, since the number of unknown FNs that need to be explored is high, while the time that can be spent on learning their performances is limited, given the user's mobility. Therefore, to address this issue, we propose to limit the number of explorations to a small subset of the FNs. As a result, the user can still have time to be served by the FN that was found to yield the lowest delay performance. Using real world mobility traces and task generation patterns, we found that it pays off to limit the number of explorations in high FN density scenarios. This is shown through significant improvements in the cumulative regret as well as the instantaneous delay, compared to the case where all newly-appeared FNs are explored.Peer ReviewedPostprint (author's final draft

Mobility-aware mechanisms for fog node discovery and selection

The recent development of delay-sensitive applications has led to the emergence of the fog computing paradigm. Within this paradigm, computation nodes present at the edge of the network can act as fog nodes (FNs) capable of processing users' tasks, thus resulting in latency reductions compared to the existing cloud-based execution model. In order to realize the full potential of fog computing, new research questions have arised, mainly due to the dynamic and heterogeneous fog computing context. This thesis focuses on the following questions in particular: How can a user detect the presence of a nearby FN? How should a user on the move adapt its FN discovery strategy, according to its changing context? How should an FN be selected , in the case of user mobility and FN mobility? These questions will be addressed throughout the different contributions of this thesis. The first contribution consists in proposing a discovery solution allowing a user to become aware of the existence of a nearby FN. Using our solution, the FN advertizes its presence using custom WiFi beacons, which will be detected by the user via a scan process. An implementation of this approach has been developed and its evaluation results have shown that it results in a non-negligible energy consumption given its use of WiFi. This has led to our second contribution, which aims at improving the WiFi scan performed in our discovery approach, especially in the case of user mobility. At a first stage, this improvement consisted in embedding information about the topology of the FNs in the beacons the user receives from previous FNs. We have shown that by adapting the scan behavior based on this information, considerable energy savings can be achieved, while guaranteeing a high discovery rate. However, as this approach is associated with a restrictive FN topology structure, we proposed a different alternative, at a second stage. This alternative leverages the history of cellular context information as an indicator allowing the user to infer whether an FN may be present in its current location. If so, the scan will be enabled. Otherwise, it is disabled. The simulation results comparing different classification algorithms have shown that a sequence-based model, such as a hidden-Markov model is able to effectively predict the FN presence in the current user location. While the previous approaches have focused on a sparse FN deployment, our third contribution considers a high density of FNs. Consequently, as there are multiple nearby FNs that can process the user's tasks, it is important to derive a suitable FN selection strategy. This strategy should consider the time-varying set of FNs caused by the user's mobility. Besides, it should minimize the number of switches from one FN to another, in order to maintain a good quality of service. With these considerations in mind, we have shown that an adaptive greedy approach, that selects an FN having a good-enough delay estimate, achieves the best results. Finally, unlike the previous contribution, where the focus has been on FN selection when the user is mobile, our final contribution deals with mobile vehicular FNs (VFNs). Given the mobility of such VFNs, it is important to make the most of their resources, since they are only available for a short time at a given area. So, we propose that, in order to select an appropriate VFN for a given task, a reference roadside unit (RSU) responsible for task assignment can use advice from a neighbor RSU. This advice consists in the VFN that will result in the lowest delay for the current task, based on the experience of the neighbor RSU. The results have shown that, using the provided advice, the reference RSU can observe significant delay reductions. All in all, the proposed contributions have addressed various problems that may arise in a fog computing context and the obtained results can be used to guide the development of the building blocks of future fog computing solutions.El recent desenvolupament d'aplicacions IoT ha comportat l'aparició del paradigma de fog computing. Dins d'aquest paradigma, els nodes de càlcul presents a la vora de la xarxa poden actuar com a “fog nodes'' (FN) capaços de processar les tasques dels usuaris, produint així reduccions de latència en comparació amb el model d'execució basat en núvol. Per assolir tot el potencial del fog computing, han sorgit noves qüestions de recerca, principalment a causa del context dinàmic i heterogeni de fog computing. Aquesta tesi se centra especialment en les qüestions següents: Com pot un usuari detectar la presència d'un FN? Com hauria d’adaptar un usuari en moviment la seva estratègia de descobriment de FN, segons el seu context? Com s’ha de seleccionar un FN, en el cas de la mobilitat dels usuaris i la mobilitat FN? Aquestes preguntes s’abordaran al llarg de les diferents aportacions d’aquesta tesi. La primera contribució consisteix a proposar una solució de descobriment que permeti a l'usuari detectar l’existència d’un FN proper. Mitjançant la nostra solució, un FN anuncia la seva presència mitjançant beacons Wi-Fi personalitzats, que seran detectats per l'usuari mitjançant un procés d’exploració. S'ha desenvolupat una implementació d'aquest enfocament i els seus resultats d’avaluació han demostrat que resulta en un consum d'energia menyspreable donat el seu ús del Wi-Fi. Això ha suposat la nostra segona contribució, que té com a objectiu millorar l’exploració Wi-Fi, especialment en el cas de la mobilitat dels usuaris. En una primera fase, aquesta millora va consistir a incorporar informació sobre la topologia dels FN en les beacons que rep l'usuari dels FN anteriors. Hem demostrat que mitjançant l'adaptació del comportament d'escaneig basat en aquesta informació es pot aconseguir un estalvi considerable d’energia, alhora que es garanteix un índex elevat de descobriment. Tanmateix, com aquest enfocament s'associa a una estructura de topologia FN restrictiva, vam proposar una alternativa diferent, en una segona etapa. Aquesta alternativa aprofita la història de la informació del context cel·lular com a indicador que permet a l'usuari deduir si un FN pot estar present en la seva ubicació. En cas afirmatiu, l'exploració estarà habilitada. Els resultats de la simulació comparant diferents algoritmes de classificació han demostrat que un model basat en seqüències, com un model HMM, és capaç de predir eficaçment la presència de FNs a la ubicació actual de l'usuari. Si bé els enfocaments anteriors s’han centrat en un desplegament escàs de FNs, la nostra tercera contribució considera una alta densitat d'FNs. En conseqüència, com que hi ha múltiples FNs propers que poden processar les tasques de l'usuari, és important derivar una estratègia de selecció de FN adequada. Aquesta estratègia hauria de tenir en compte el conjunt variable de temps causat per la mobilitat de l'usuari. A més, hauria de minimitzar el nombre de canvis d'un FN a un altre, per mantenir una bona qualitat del servei. Tenint en compte aquestes consideracions, hem demostrat que un enfocament codiciós adaptatiu, que selecciona un FN amb una estimació de retard suficient, aconsegueix els millors resultats. Finalment, a diferència de l'aportació anterior, on l'atenció s'ha fixat en la selecció d'FN quan l'usuari és mòbil, la nostra contribució final tracta sobre les FNs per a vehicles mòbils (VFNs). Tenint en compte la mobilitat d’aquests VFNs, és important aprofitar al màxim els seus recursos, ja que només estan disponibles per a un temps curt. Així doncs, proposem que, per seleccionar un VFN adequat per a una tasca, una unitat RSU responsable de l'assignació de tasques pot utilitzar consells d'un RSU veí. Aquest consell consisteix en escollir el VFN que suposarà el menor retard de la tasca actual, en funció de l’experiència del RSU veí. Els resultats han demostrat que ..

Mobility-aware mechanisms for fog node discovery and selection

The recent development of delay-sensitive applications has led to the emergence of the fog computing paradigm. Within this paradigm, computation nodes present at the edge of the network can act as fog nodes (FNs) capable of processing users' tasks, thus resulting in latency reductions compared to the existing cloud-based execution model. In order to realize the full potential of fog computing, new research questions have arised, mainly due to the dynamic and heterogeneous fog computing context. This thesis focuses on the following questions in particular: How can a user detect the presence of a nearby FN? How should a user on the move adapt its FN discovery strategy, according to its changing context? How should an FN be selected , in the case of user mobility and FN mobility? These questions will be addressed throughout the different contributions of this thesis. The first contribution consists in proposing a discovery solution allowing a user to become aware of the existence of a nearby FN. Using our solution, the FN advertizes its presence using custom WiFi beacons, which will be detected by the user via a scan process. An implementation of this approach has been developed and its evaluation results have shown that it results in a non-negligible energy consumption given its use of WiFi. This has led to our second contribution, which aims at improving the WiFi scan performed in our discovery approach, especially in the case of user mobility. At a first stage, this improvement consisted in embedding information about the topology of the FNs in the beacons the user receives from previous FNs. We have shown that by adapting the scan behavior based on this information, considerable energy savings can be achieved, while guaranteeing a high discovery rate. However, as this approach is associated with a restrictive FN topology structure, we proposed a different alternative, at a second stage. This alternative leverages the history of cellular context information as an indicator allowing the user to infer whether an FN may be present in its current location. If so, the scan will be enabled. Otherwise, it is disabled. The simulation results comparing different classification algorithms have shown that a sequence-based model, such as a hidden-Markov model is able to effectively predict the FN presence in the current user location. While the previous approaches have focused on a sparse FN deployment, our third contribution considers a high density of FNs. Consequently, as there are multiple nearby FNs that can process the user's tasks, it is important to derive a suitable FN selection strategy. This strategy should consider the time-varying set of FNs caused by the user's mobility. Besides, it should minimize the number of switches from one FN to another, in order to maintain a good quality of service. With these considerations in mind, we have shown that an adaptive greedy approach, that selects an FN having a good-enough delay estimate, achieves the best results. Finally, unlike the previous contribution, where the focus has been on FN selection when the user is mobile, our final contribution deals with mobile vehicular FNs (VFNs). Given the mobility of such VFNs, it is important to make the most of their resources, since they are only available for a short time at a given area. So, we propose that, in order to select an appropriate VFN for a given task, a reference roadside unit (RSU) responsible for task assignment can use advice from a neighbor RSU. This advice consists in the VFN that will result in the lowest delay for the current task, based on the experience of the neighbor RSU. The results have shown that, using the provided advice, the reference RSU can observe significant delay reductions. All in all, the proposed contributions have addressed various problems that may arise in a fog computing context and the obtained results can be used to guide the development of the building blocks of future fog computing solutions.El recent desenvolupament d'aplicacions IoT ha comportat l'aparició del paradigma de fog computing. Dins d'aquest paradigma, els nodes de càlcul presents a la vora de la xarxa poden actuar com a “fog nodes'' (FN) capaços de processar les tasques dels usuaris, produint així reduccions de latència en comparació amb el model d'execució basat en núvol. Per assolir tot el potencial del fog computing, han sorgit noves qüestions de recerca, principalment a causa del context dinàmic i heterogeni de fog computing. Aquesta tesi se centra especialment en les qüestions següents: Com pot un usuari detectar la presència d'un FN? Com hauria d’adaptar un usuari en moviment la seva estratègia de descobriment de FN, segons el seu context? Com s’ha de seleccionar un FN, en el cas de la mobilitat dels usuaris i la mobilitat FN? Aquestes preguntes s’abordaran al llarg de les diferents aportacions d’aquesta tesi. La primera contribució consisteix a proposar una solució de descobriment que permeti a l'usuari detectar l’existència d’un FN proper. Mitjançant la nostra solució, un FN anuncia la seva presència mitjançant beacons Wi-Fi personalitzats, que seran detectats per l'usuari mitjançant un procés d’exploració. S'ha desenvolupat una implementació d'aquest enfocament i els seus resultats d’avaluació han demostrat que resulta en un consum d'energia menyspreable donat el seu ús del Wi-Fi. Això ha suposat la nostra segona contribució, que té com a objectiu millorar l’exploració Wi-Fi, especialment en el cas de la mobilitat dels usuaris. En una primera fase, aquesta millora va consistir a incorporar informació sobre la topologia dels FN en les beacons que rep l'usuari dels FN anteriors. Hem demostrat que mitjançant l'adaptació del comportament d'escaneig basat en aquesta informació es pot aconseguir un estalvi considerable d’energia, alhora que es garanteix un índex elevat de descobriment. Tanmateix, com aquest enfocament s'associa a una estructura de topologia FN restrictiva, vam proposar una alternativa diferent, en una segona etapa. Aquesta alternativa aprofita la història de la informació del context cel·lular com a indicador que permet a l'usuari deduir si un FN pot estar present en la seva ubicació. En cas afirmatiu, l'exploració estarà habilitada. Els resultats de la simulació comparant diferents algoritmes de classificació han demostrat que un model basat en seqüències, com un model HMM, és capaç de predir eficaçment la presència de FNs a la ubicació actual de l'usuari. Si bé els enfocaments anteriors s’han centrat en un desplegament escàs de FNs, la nostra tercera contribució considera una alta densitat d'FNs. En conseqüència, com que hi ha múltiples FNs propers que poden processar les tasques de l'usuari, és important derivar una estratègia de selecció de FN adequada. Aquesta estratègia hauria de tenir en compte el conjunt variable de temps causat per la mobilitat de l'usuari. A més, hauria de minimitzar el nombre de canvis d'un FN a un altre, per mantenir una bona qualitat del servei. Tenint en compte aquestes consideracions, hem demostrat que un enfocament codiciós adaptatiu, que selecciona un FN amb una estimació de retard suficient, aconsegueix els millors resultats. Finalment, a diferència de l'aportació anterior, on l'atenció s'ha fixat en la selecció d'FN quan l'usuari és mòbil, la nostra contribució final tracta sobre les FNs per a vehicles mòbils (VFNs). Tenint en compte la mobilitat d’aquests VFNs, és important aprofitar al màxim els seus recursos, ja que només estan disponibles per a un temps curt. Així doncs, proposem que, per seleccionar un VFN adequat per a una tasca, una unitat RSU responsable de l'assignació de tasques pot utilitzar consells d'un RSU veí. Aquest consell consisteix en escollir el VFN que suposarà el menor retard de la tasca actual, en funció de l’experiència del RSU veí. Els resultats han demostrat que ...Postprint (published version

Towards a context-aware Wi-Fi-based Fog Node discovery scheme using cellular footprints

Recently, new computing paradigms such as fog and edge computing started to emerge in an attempt to cope with the low latency requirements of new classes of IoT applications. In order for these paradigms to fully realize their potential, an important challenge to address is the discovery of fog nodes (FNs) with spare computational resources that can be used to host time- sensitive and computationally intensive application components. We particularly focus on this problem in this paper, considering the case of a WiFi-based FN discovery process. More specifically, we evaluate how practical it is to trigger the discovery of fog nodes based on a mobile phone's historical cellular footprints in order to obtain a high discovery rate and a low energy overhead. To this end, we conducted a small-scale cellular data collection to be used to test different learning approaches, including the K-Nearest Neighbors and the decision tree algorithms as well as a Hidden Markov Model (HMM). According to our evaluation results, HMM was found to achieve the maximum discovery and energy saving ratios. The impact of initial FN misdetections on the user-FN contact ratio has also been studied.Peer ReviewedPostprint (published version

Towards user-centric, switching cost-aware fog node selection strategies

In order to address high latency issues that may arise when executing timecritical applications at the cloud side, the novel fog computing paradigm has emerged, thus enabling the execution of such applications within computation nodes present at the edge of the network. While executing such applications, a user may be moving in an area where a high number of heterogeneous fog nodes (FNs) co-exist. This makes the problem of selecting the most appropriate fog node to execute the user’s tasks challenging, especially since the set of visible FNs dynamically changes. Therefore, to deal with the uncertain and dynamic nature of such a fog computing environment, we model the FN selection problem using multi-armed bandits. However, standard solutions for the bandit problem are not tailored for scenarios with changing FN availabilities. In addition, since switching from one FN to the other causes a switching cost, such solutions lead to accumulating a high switching cost. Therefore, to address these issues, we first propose a block-based FN selection scheme, where switching among FNs is not allowed during a block of timeslots. We also propose a greedy approach, where FNs having a sufficiently good delay performance are selected in a greedy manner. Simulation results reveal that both approaches significantly improve the FN selection performance. In particular, we found that the blockbased selection results in the lowest switching costs, whereas the greedy selection achieves the best overall performance.Peer ReviewedPostprint (author's final draft

A Beacon-assisted direction-aware scanning scheme for 802.11-based discovery in Fog-to-Cloud systems

In order to fully leverage idle computational resources in a fog computing scenario, a suitable discovery mechanism is needed. Within this context, F2C-Aware has been proposed in a previous work as a discovery solution where a device can detect nearby fog resources by scanning for custom 802.11 beacons being broadcast by those resources. Since mobility is an inherent characteristic in fog computing, keeping the Wi-Fi-based discovery service always on during mobility will lead to unnecessary scans being performed in non-covered areas, causing energy consumption penalties. That is why, in this paper, we propose a Beacon-assisted Direction-aware Scanning Scheme (BDSS), where we use the discovery beacon to convey the distance remaining until a next fog will be reached in each of the four cardinal directions along with the channel used for beacon advertisements. Therefore, the scan will be disabled until the next fog is reached. Emulation results show that by using this scheme, considerable energy savings could be made while maintaining a high discovery rate.Peer ReviewedPostprint (published version

Analyzing the deployment challenges of beacon stuffing as a discovery enabler in Fog-to-Cloud systems

In order to meet the needs of emerging IoT applications having tight QoS constraints, new computing paradigms have been proposed, bringing computation resources closer to the edge of the network, where IoT resides. One of such paradigms is Fog-to-Cloud (F2C), defined as a framework where the combined use of fog and cloud resources is coordinated and managed in an optimized manner to achieve the desired service requirements. Unlike cloud computing, the fog provides a heterogeneous set of resources, possibly within fixed deployments provided by city managers, or that could even be contributed by end users. This brings in many challenges yet to be addressed such as resource discovery, which is the focus of this paper. This paper digs into the utilization of 802.11 beacon stuffing as a possible solution allowing the discovery of nearby devices in an F2C system, particularly dealing with specific design and implementation details of the proposed solution and more importantly its real applicability to an F2C system through the analysis of several experiments carried out on a real world testbed.Peer Reviewe

An Energy-aware End-to-End Crowdsensing Platform: Sensarena

International audienceNowadays, smart-devices come with a rich set of built-in sensors besides being full-fledged processing and communicating handsets. This empowers the crowd to collect and share sensed data about various city-related phenomena, a new paradigm denoted as Crowdsensing. In this context, we introduce Sensarena; an end-to-end general-use crowdsensing platform which consists of three main elements: two different android- based applications and a central server. The first mobile application is destined to the participants to conduct sensing campaigns and the second is for requestors to submit their sensing requests. Besides, the server side is designed to host energy-aware sensing tasks assignment mechanisms and storage of different types of data. The developed platform has been exhaustively tested for different scenarios and proved a competitive performance while responding to both participants and requestors requirements