A decentralized paradigm for resource-aware computing in wireless Ad hoc networks

A key factor limiting the democratisation of networked systems is the lack of trust, particularly in the wake of data-intensive applications that work on sensitive and private data, which requires providing strong network security guarantees via encryption and authentication algorithms, as well as rethinking algorithms to compute on the network peripheries without moving data. In many security and privacy-critical domains such as Home Automation IoT networks, AUV networks etc., the existence of a centralized privileged node leads to a vulnerability for leakage of sensitive information. In this paper, we have proposed a decentralized networking architecture that adopts collaborative processing techniques and operates within the tradeoff between network security and performance. We have investigated the design and sustainability of autonomous decentralized systems and evaluated the efficiency of the proposed scheme with the help of extensive simulation tools

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented

Detection of Behavioral Malware in Delay Tolerant Networks

Disruption-tolerant networking has gained currency in the United States due to support from DARPA, which has funded many DTN projects. Disruption may occur because of the limits of wireless radio range, sparsity of mobile nodes, energy resources, attack, and noise. The delay-tolerant-network (DTN) model is becoming a viable communication alternative to the traditional infrastructural model for modern mobile consumer electronics equipped with short-range communication technologies such as Bluetooth, NFC, and Wi-Fi Direct. Proximity malware is a class of malware that exploits the opportunistic contacts and distributed nature of DTNs for propagation. Behavioral characterization of malware is an effective alternative to pattern matching in detecting malware, especially when dealing with polymorphic or obfuscated malware. In this paper, we first propose a general behavioral characterization of proximity malware which based on Naive Bayesian model, which has been successfully applied in non-DTN settings such as filtering email spams and detecting bonnets. We identify two unique challenges for extending Bayesian malware detection to DTNs (“insufficient evidence vs. evidence collection risk” and “filtering false evidence sequentially and distributedly”), and propose a simple yet effective method, look-ahead, to address the challenges. Furthermore, we propose two extensions to look-ahead, dogmatic filtering and adaptive look-ahead, to address the challenge of “malicious nodes sharing false evidence”. Real mobile network traces are used to verify the effectiveness of the proposed methods

A trust framework for peer-to-peer interaction in ad hoc networks

PhDAs a wider public is increasingly adopting mobile devices with diverse applications, the idea of who to trust while on the move becomes a crucial one. The need to find dependable partners to interact is further exacerbated in situations where one finds oneself out of the range of backbone structures such as wireless base stations or cellular networks. One solution is to generate self-started networks, a variant of which is the ad hoc network that promotes peer-to-peer networking. The work in this thesis is aimed at defining a framework for such an ad hoc network that provides ways for participants to distinguish and collaborate with their most trustworthy neighbours. In this framework, entities create the ability to generate trust information by directly observing the behaviour of their peers. Such trust information is also shared in order to assist those entities in situations where prior interactions with their target peers may not have existed. The key novelty points of the framework focus on aggregating the trust evaluation process around the most trustworthy nodes thereby creating a hierarchy of nodes that are distinguished by the class, defined by cluster heads, to which they belong. Furthermore, the impact of such a framework in generating additional overheads for the network is minimised through the use of clusters. By design, the framework also houses a rule-based mechanism to thwart misbehaving behaviour or non-cooperation. Key performance indicators are also defined within this work that allow a framework to be quickly analysed through snapshot data, a concept analogous to those used within financial circles when assessing companies. This is also a novel point that may provide the basis for directly comparing models with different underlying technologies. The end result is a trust framework that fully meets the basic requirements for a sustainable model of trust that can be developed onto an ad hoc network and that provides enhancements in efficiency (using clustering) and trust performance

Software reliability and dependability: a roadmap

Shifting the focus from software reliability to user-centred measures of dependability in complete software-based systems. Influencing design practice to facilitate dependability assessment. Propagating awareness of dependability issues and the use of existing, useful methods. Injecting some rigour in the use of process-related evidence for dependability assessment. Better understanding issues of diversity and variation as drivers of dependability. Bev Littlewood is founder-Director of the Centre for Software Reliability, and Professor of Software Engineering at City University, London. Prof Littlewood has worked for many years on problems associated with the modelling and evaluation of the dependability of software-based systems; he has published many papers in international journals and conference proceedings and has edited several books. Much of this work has been carried out in collaborative projects, including the successful EC-funded projects SHIP, PDCS, PDCS2, DeVa. He has been employed as a consultant t

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

[EN] If last decade viewed computational services as a utility then surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of ¿platform as a service¿ or ¿software as a service¿, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.Garcia Valls, MS.; Dubey, A.; Botti, V. (2018). Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges. Journal of Systems Architecture. 91:83-102. https://doi.org/10.1016/j.sysarc.2018.05.007S831029