An eco-friendly hybrid urban computing network combining community-based wireless LAN access and wireless sensor networking

Computer-enhanced smart environments, distributed environmental monitoring, wireless communication, energy conservation and sustainable technologies, ubiquitous access to Internet-located data and services, user mobility and innovation as a tool for service differentiation are all significant contemporary research subjects and societal developments. This position paper presents the design of a hybrid municipal network infrastructure that, to a lesser or greater degree, incorporates aspects from each of these topics by integrating a community-based Wi-Fi access network with Wireless Sensor Network (WSN) functionality. The former component provides free wireless Internet connectivity by harvesting the Internet subscriptions of city inhabitants. To minimize session interruptions for mobile clients, this subsystem incorporates technology that achieves (near-)seamless handover between Wi-Fi access points. The WSN component on the other hand renders it feasible to sense physical properties and to realize the Internet of Things (IoT) paradigm. This in turn scaffolds the development of value-added end-user applications that are consumable through the community-powered access network. The WSN subsystem invests substantially in ecological considerations by means of a green distributed reasoning framework and sensor middleware that collaboratively aim to minimize the network's global energy consumption. Via the discussion of two illustrative applications that are currently being developed as part of a concrete smart city deployment, we offer a taste of the myriad of innovative digital services in an extensive spectrum of application domains that is unlocked by the proposed platform

Privacy Protection for Wi-Fi Location Positioning Systems

International audienceWith the democratization of mobile devices embedding different positioning capabilities, location information is used for a variety of applications. On mobile devices, the geolocation can be obtained via GPS or by leveraging surrounding network infrastructure such as Wi-Fi access points. Despite a lower accuracy, Wi-Fi based geolocation has several advantages over GPS such as reduced energy consumption and availability in indoor environments. To enable this network-based geolocation, mobile devices need to interact with a location positioning system that will resolve a list of visible Wi-Fi access points into a position. By doing so, mobile users are revealing their mobility to the location provider, potentially exposing sensitive information to an untrusted third-party. In this paper, we propose a novel solution to preserve users' privacy when requesting users' location from Wi-Fi while supporting high utility. The key idea behind our online approach is to combine a caching strategy (for limiting the exposure of the user's position for already visited locations) and a random sampling (for controlling the precision of revealed information). We extensively evaluate our solution with a real dataset of mobility traces. We show that the proposed approach drastically reduces the exposure of the user's location to positioning systems (up to 95%). Indeed, by leveraging a caching strategy, requests are only sent when users visit new areas. Consequently, the capacity of positioning systems to infer points of interest of users from received requests is highly limited (a decrease of 50% on average). In addition, our privacy protection provides a trade-off between privacy (i.e., avoid revealing its true location) and utility (i.e., still benefiting from services such as places recommendation) fully controllable by the users

On the traffic offloading in Wi-Fi supported heterogeneous wireless networks

Heterogeneous small cell networks (HetSNet) comprise several low power, low cost (SBSa), (D2D) enabled links wireless-fidelity (Wi-Fi) access points (APs) to support the existing macrocell infrastructure, decrease over the air signaling and energy consumption, and increase network capacity, data rate and coverage. This paper presents an active user dependent path loss (PL) based traffic offloading (TO) strategy for HetSNets and a comparative study on two techniques to offload the traffic from macrocell to (SBSs) for indoor environments: PL and signal-to-interference ratio (SIR) based strategies. To quantify the improvements, the PL based strategy against the SIR based strategy is compared while considering various macrocell and (SBS) coverage areas and traffic–types. On the other hand, offloading in a dense urban setting may result in overcrowding the (SBSs). Therefore, hybrid traffic–type driven offloading technologies such as (WiFi) and (D2D) were proposed to en route the delay tolerant applications through (WiFi) (APs) and (D2D) links. It is necessary to illustrate the impact of daily user traffic profile, (SBSs) access schemes and traffic–type while deciding how much of the traffic should be offloaded to (SBSs). In this context, (AUPF) is introduced to account for the population of active small cells which depends on the variable traffic load due to the active users

Crowdsourcing based Room Localization on Smartphones

People spend approximately 90% of their time indoors, and human indoor activities are strongly correlated with the rooms they are in. Room localization, which identifies the room a person or smartphone is in, provides a powerful tool for characterizing human indoor activities and helping address challenges in public health, productivity, building management, etc. Designing a room localization system that is practically useful in real-world environments is challenging. First, due to the complex multi-path propagation problem, Wi-Fi signals obtained by smartphones are dynamic and noisy. Such noise obscures the unique relationship between Wi-Fi signals and individual rooms. Second, existing room localization methods require labor-intensive manual annotation of individual rooms. The process is time-consuming and expensive, which is a key limitation of existing room localization applications. Third, knowledge of indoor floorplans is often required by room localization applications. However, indoor floorplans are either unavailable or obtaining them requires slow, tedious, and error-prone manual labor. In addition, the overhead of room localization, e.g., energy consumption, imposed on personal smartphones must be low. To tackle those challenges, this thesis proposed a set of techniques: (1) an accurate temporal n-gram augmented Bayesian room positioning method that leverages the ordered sequence information of access points and users' daily motion pattern among rooms; (2) an automatic room fingerprinting approach that identifies in-room occupancy ``hotspot(s)" using density of Wi-Fi signals, and then learns the inter-zone correlation -- thereby distinguishing different rooms; (3) an automatic floorplan construction method that determines the geometries of individual rooms, as well as room adjacency information, and then constructs an indoor floorplan through hallway layout learning and force directed dilation; and (4) an energy-efficient trip detection framework that consists of two modes: the deliberation mode learns cell-id patterns using GPS/Wi-Fi based localization methods, and the intuition mode only uses cell-ids and learned patterns for trip detection

Indoor positioning with deep learning for mobile IoT systems

2022 Summer.Includes bibliographical references.The development of human-centric services with mobile devices in the era of the Internet of Things (IoT) has opened the possibility of merging indoor positioning technologies with various mobile applications to deliver stable and responsive indoor navigation and localization functionalities that can enhance user experience within increasingly complex indoor environments. But as GPS signals cannot easily penetrate modern building structures, it is challenging to build reliable indoor positioning systems (IPS). Currently, Wi-Fi sensing based indoor localization techniques are gaining in popularity as a means to build accurate IPS, benefiting from the prevalence of 802.11 family. Wi-Fi fingerprinting based indoor localization has shown remarkable performance over geometric mapping in complex indoor environments by taking advantage of pattern matching techniques. Today, the two main information extracted from Wi-Fi signals to form fingerprints are Received Signal Strength Index (RSSI) and Channel State Information (CSI) with Orthogonal Frequency-Division Multiplexing (OFDM) modulation, where the former can provide the average localization error around or under 10 meters but has low hardware and software requirements, while the latter has a higher chance to estimate locations with ultra-low distance errors but demands more resources from chipsets, firmware/software environments, etc. This thesis makes two novel contributions towards realizing viable IPS on mobile devices using RSSI and CSI information, and deep machine learning based fingerprinting. Due to the larger quantity of data and more sophisticated signal patterns to create fingerprints in complex indoor environments, conventional machine learning algorithms that need carefully engineered features suffer from the challenges of identifying features from very high dimensional data. Hence, the abilities of approximation functions generated from conventional machine learning models to estimate locations are limited. Deep machine learning based approaches can overcome these challenges to realize scalable feature pattern matching approaches such as fingerprinting. However, deep machine learning models generally require considerable memory footprint, and this creates a significant issue on resource-constrained devices such as mobile IoT devices, wearables, smartphones, etc. Developing efficient deep learning models is a critical factor to lower energy consumption for resource intensive mobile IoT devices and accelerate inference time. To address this issue, our first contribution proposes the CHISEL framework, which is a Wi-Fi RSSI- based IPS that incorporates data augmentation and compression-aware two-dimensional convolutional neural networks (2D CAECNNs) with different pruning and quantization options. The proposed model compression techniques help reduce model deployment overheads in the IPS. Unlike RSSI, CSI takes advantages of multipath signals to potentially help indoor localization algorithms achieve a higher level of localization accuracy. The compensations for magnitude attenuation and phase shifting during wireless propagation generate different patterns that can be utilized to define the uniqueness of different locations of signal reception. However, all prior work in this domain constrains the experimental space to relatively small-sized and rectangular rooms where the complexity of building interiors and dynamic noise from human activities, etc., are seldom considered. As part of our second contribution, we propose an end-to-end deep learning based framework called CSILoc for Wi-Fi CSI-based IPS on mobile IoT devices. The framework includes CSI data collection, clustering, denoising, calibration and classification, and is the first study to verify the feasibility to use CSI for floor level indoor localization with minimal knowledge of Wi-Fi access points (APs), thus avoiding security concerns during the offline data collection process

Energy Consumption of Wireless Network Access Points

2nd International Conference on Green Communications and Networking, GreeNets 2012; Gandia; Spain; 25 October 2012 through 26 October 2012The development of low cost technology based on IEEE 802.11 standard permits to build telecommunication networks at low cost, allowing providing Internet access in rural areas in developing countries. The lack of access to the electrical grid is a problem when the network is being developed in rural areas, so that wireless access points should operate using solar panels and batteries. Many cases can be found where the energy consumption becomes a key point in wireless network design. In this paper we present a comparative study of the energy consumption of several wireless network access points. We will compare the energy consumption of different brands and models, for several operation scenarios and operating modes. Obtained results allow us to achieve the objective of this article, that is, promote the development of wireless communication networks energetically efficient.Andrade Morelli, S.; Ruiz Sanchez, E.; Granell Romero, E.; Lloret, J. (2013). Energy Consumption of Wireless Network Access Points. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST. 113:81-91. doi:10.1007/978-3-642-37977-2_8S8191113Khoa Nguyen, K., Jaumard, B.: Routing Engine Architecture for Next Generation Routers: Evolutional Trends. Network Protocols and Algorithms 1(1), 62–85 (2009)IEEE Std 802.11: IEEE Standard for Information technology -Telecommunications and information exchange between systems -Local and metropolitan area networks - Specific requirements – Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. Institute of Electrical and Electronics Engineers, New York, USA, pp.1–1184 (2007)Lloret, J., Garcia, M., Bri, D., Sendra, S.: A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification. Sensors 9(11), 8722–8747 (2009)Tapia, A., Maitland, C., Stone, M.: Making IT work for Municipalities: Building municipal wireless networks. Government Information Quarterly 23(3), 359–380 (2006)van Drunen, R., Koolhaas, J., Schuurmans, H., Vijn, M.: Building a Wireless Community Network in the Netherland. In: USENIX 2003 / Freenix Annual Technical Conference Proceedings, San Antonio, Texas, USA, June 9-14, pp. 219–230 (2003)Powell, A., Shade, L.R.: Going Wi-Fi in Canada: Municipal and Community Initiatives. Canadian Research Alliance for Community Innovation and Networking (2005)Sendra, S., Fernández, P.A., Quilez, M.A., Lloret, J.: Study and Performance of Interior Gateway IP routing Protocols. Network Protocols and Algorithms 2(4), 88–117 (2010)Galperin, H.: Wireless Networks and Rural Development: Opportunities for Latin America. Information Technologies and International Development 2(3), 47–56 (2005)Segal, M.: Improving lifetime of wireless sensor networks. Network Protocols and Algorithms 1(2), 48–60 (2009)Momani, A.A.E., Yassein, M.B., Darwish, O., Manaseer, S., Mardini, W.: Intelligent Paging Backoff Algorithm for IEEE 802.11 MAC Protocol. Network Protocols and Algorithms 4(2), 108–123 (2012)Mohsin, A.H., Bakar, K.A., Adekiigbe, A., Ghafoor, K.Z.: A Survey of Energy-aware Routing protocols in Mobile Ad-hoc Networks: Trends and Challenges. Network Protocols and Algorithms 4(2), 82–107 (2012)Feeney, L.M., Nilsson, M.: Investigating the Energy Consumption of a Wireless Network Interface in an Ad Hoc Networking Environment. In: Proceedings of the Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2001, Anchorage, Alaska, April 22-26, vol. 3, pp. 1548–1557. IEEE (2001)Barbancho, J., León, C., Molina, F.J., Barbancho, A.: Using artificial intelligence in routing schemes for wireless networks. Computer Communications 30(14-15), 2802–2811 (2007)Tao, C., Yang, Y., Honggang, Z., Haesik, K., Horneman, K.: Network energy saving technologies for green wireless access networks. IEEE Wireless Communications 18(5), 30–38 (2011)Sendra, S., Lloret, J., Garcia, M., Toledo, J.F.: Power saving and energy optimization techniques for Wireless Sensor Networks. Journal of Communications 6(6), 439–459 (2011

iSEA: IoT-based smartphone energy assistant for prompting energy-aware behaviors in commercial buildings

Providing personalized energy-use information to individual occupants enables the adoption of energy-aware behaviors in commercial buildings. However, the implementation of individualized feedback still remains challenging due to the difficulties in collecting personalized data, tracking personal behaviors, and delivering personalized tailored information to individual occupants. Nowadays, the Internet of Things (IoT) technologies are used in a variety of applications including real-time monitoring, control, and decision-making due to the flexibility of these technologies for fusing different data streams. In this paper, we propose a novel IoT-based smartphone energy assistant (iSEA) framework which prompts energy-aware behaviors in commercial buildings. iSEA tracks individual occupants through tracking their smartphones, uses a deep learning approach to identify their energy usage, and delivers personalized tailored feedback to impact their usage. iSEA particularly uses an energy-use efficiency index (EEI) to understand behaviors and categorize them into efficient and inefficient behaviors. The iSEA architecture includes four layers: physical, cloud, service, and communication. The results of implementing iSEA in a commercial building with ten occupants over a twelve-week duration demonstrate the validity of this approach in enhancing individualized energy-use behaviors. An average of 34% energy savings was measured by tracking occupants’ EEI by the end of the experimental period. In addition, the results demonstrate that commercial building occupants often ignore controlling over lighting systems at their departure events that leads to wasting energy during non-working hours. By utilizing the existing IoT devices in commercial buildings, iSEA significantly contributes to support research efforts into sensing and enhancing energy-aware behaviors at minimal costs