Abdelrahman Abuarqoub

Dynamic clustering and management of mobile wireless sensor networks

In Wireless Sensor Networks (WSNs), routing data towards the sink leads to unbalanced energy consumption among intermediate nodes resulting in high data loss rate. The use of multiple Mobile Data Collectors (MDCs) has been proposed in the literature to mitigate such problems. MDCs help to achieve uniform energy-consumption across the network, fill coverage gaps, and reduce end-to-end communication delays, amongst others. However, mechanisms to support MDCs such as location advertisement and route maintenance introduce significant overhead in terms of energy consumption and packet delays. In this paper, we propose a self-organizing and adaptive Dynamic Clustering (DCMDC) solution to maintain MDC-relay networks. This solution is based on dividing the network into well-delimited clusters called Service Zones (SZs). Localizing mobility management traffic to a SZ reduces signaling overhead, route setup delay and bandwidth utilization. Network clustering also helps to achieve scalability and load balancing. Smaller network clusters make buffer overflows and energy depletion less of a problem. These performance gains are expected to support achieving higher information completeness and availability as well as maximizing the network lifetime. Moreover, maintaining continuous connectivity between the MDC and sensor nodes increases information availability and validity. Performance experiments show that DCMDC outperforms its rival in the literature. Besides the improved quality of information, the proposed approach improves the packet delivery ratio by up to 10%, end-to-end delay by up to 15%, energy consumption by up to 53%, energy balancing by up to 51%, and prolongs the network lifetime by up to53%.

A Wireless Sensor Network Border Monitoring System: Deployment Issues and Routing Protocols

External border surveillance is critical to the security of every state and the challenges it poses are changing and likely to intensify. Wireless sensor networks (WSN) are a low cost technology that provide an intelligence-led solution to effective continuous monitoring of large, busy, and complex landscapes. The linear network topology resulting from the structure of the monitored area raises challenges that have not been adequately addressed in the literature to date. In this paper, we identify an appropriate metric to measure the quality of WSN border crossing detection. Furthermore, we propose a method to calculate the required number of sensor nodes to deploy in order to achieve a specified level of coverage according to the chosen metric in a given belt region, while maintaining radio connectivity within the network. Then, we contribute a novel cross layer routing protocol, called levels division graph (LDG), designed specifically to address the communication needs and link reliability for topologically linear WSN applications. The performance of the proposed protocol is extensively evaluated in simulations using realistic conditions and parameters. LDG simulation results show significant performance gains when compared with its best rival in the literature, dynamic source routing (DSR). Compared with DSR, LDG improves the average end-to-end delays by up to 95%, packet delivery ratio by up to 20%, and throughput by up to 60%, while maintaining comparable performance in terms of normalized routing load and energy consumption.

An Overview of Information Extraction from Mobile Wireless Sensor Networks

Information Extraction (IE) is a key research area within the field of Wireless Sensor Networks (WSNs). It has been characterised in a variety of ways, ranging from the description of its purposes, to reasonably abstract models of its processes and components. There has been only a handful of papers addressing IE over mobile WSNs directly, these dealt with individual mobility related problems as the need arises. This paper is presented as a tutorial that takes the reader from the point of identifying data about a dynamic (mobile) real world problem, relating the data back to the world from which it was collected, and finally discovering what is in the data. It covers the entire process with special emphasis on how to exploit mobility in maximising information return from a mobile WSN. We present some challenges introduced by mobility on the IE process as well as its effects on the quality of the extracted information. Finally, we identify future research directions facing the development of efficient IE approaches for WSNs in the presence of mobility.

Unmanned Ground Vehicle for Data Collection in Wireless Sensor Networks: Mobility-aware Sink Selection

Several recent studies have demonstrated the benefits of using the Wireless Sensor Network (WSN) technology in large-scale monitoring applications, such as planetary exploration and battlefield surveillance. Sensor nodes generate continuous stream of data, which must be processed and delivered to end users in a timely manner. This is a very challenging task due to constraints in sensor node’s hardware resources. Mobile Unmanned Ground Vehicles (UGV) has been put forward as a solution to increase network lifetime and to improve systems Quality of Service (QoS). UGV are mobile devices that can move closer to data sources to reduce the bridging distance to the sink. They gather and process sensory data before they transmit it over a long-range communication technology. In large-scale monitored physical environments, the deployment of multiple-UGV is essential to deliver consistent QoS across different parts of the network. However, data sink mobility causes intermittent connectivity and high re-connection overhead, which may introduce considerable data delivery delay. Consequently, frequent network reconfigurations in multiple data sink networks must be managed in an effective way. In this paper, we contribute an algorithm to allow nodes to choose between multiple available UGVs, with the primary objective of reducing the network reconfiguration and signalling overhead. This is realised by assigning each node to the mobile sink that offers the longest connectivity time. The proposed algorithm takes into account the UGV’s mobility parameters, including its movement direction and velocity, to achieve longer connectivity period. Experimental results show that the proposed algorithm can reduce end-to-end delay and improve packet delivery ratio, while maintaining low sink discovery and handover overhead. When compared to its best rivals in the literature, the proposed approach improves the packet delivery ratio by up to 22%, end-to-end delay by up to 28%, energy consumption by up to 58%, and doubles the network lifetime.

Behaviour Profiling in Healthcare Applications Using the Internet of Things Technology

This position paper advocates applying the monitoring pogwer of IoT to build profiles of user behaviour using the large volumes of collected data. The desired system exploits sensor data mining approaches to profile user behaviour patterns in smart environments. Sensor data is mined to extract relationships of interest between environmental variables (context) and the user, building in this way behaviour profiles. The capability of applying knowledge to manipulate user’s environment is expected take monitoring beyond the simple alert-mode of operation to long term profiling of user’s behaviour. After a brief literature review to prove the suitability of IoT as a low-cost unsupervised profiling platform, we give the details of our proposal and the objectives that needs to be met before user behaviour profiling across inter-spaces is possible.

A Service-Centric Stack for Collaborative Data Sharing and Processing

A ubiquitous embedded network, such as wireless sensor network, is characterised by its capability to carry out common tasks by sharing resources that are placed in-node or in-network domains. While new application domains are emerging, the challenge is in the interpretation and utilisation of heterogeneous data generated by different types of interfaces. In this paper, a new service-centric framework for data sharing and manipulation is introduced. This framework views nodes as lightweight, composite services. Services can be composed among nodes in the same network region to extract geo-spatial and temporal patterns of the sensed data. We propose Coordination models and languages for integrating heterogeneous services together by interfacing with each service to accomplish the network mission based on the data they collect and process. This paper presents a scenario that demonstrate how coordination rules can be used to support effective collaboration between sensor nodes to extract information.

A Survey on Internet of Things Enabled Smart Campus Applications

The fictional future home, workspace or city, as predicted by science TV shows of the 1960s, is now a reality. Modern microelectronics and communication technologies offer the type of smart living that looked practically inconceivable just a few decades ago. The Internet of Things (IoT) is one of the main drivers of the future smart spaces. It enables new operational technologies and offers vital financial and environmental benefits. With IoT, spaces are evolving from being just smart to become intelligent and connected. This survey paper focuses on how to leverage IoT technologies to build a modular approach to smart campuses. The paper identifies the key benefits and motivation behind the development of IoT-enabled campus. Then, it provides a comprehensive view of general types of smart campus applications. Finally, we consider the vital design challenges that has to be met to realise a smart campus.

Optimal Mobile Sink Selection Scheme for Multiple Sink Mobile Wireless Sensor Networks

One of the most critical properties of a wireless sensor network is the adaptivity to topological changes. Several studies have demonstrated the benefits of using mobile nodes as an effective tool for gathering data from sensor nodes. However, nodes mobility causes steady connectivity disruptions, where the delay between two disruptions is dependent on the node movement speed and direction. In this paper, we introduce an approach, which based on the connection expiry time, to cluster nodes around a moving mobile sink. Sensor nodes joins the mobile sink that offers the longest connectivity time and the lowest communication overhead. When compared to its best rivals in the literature, the proposed approach improves the packet delivery ratio by up to 21%, end-to-end delay by up to 28% and energy consumption by up to 42%.

Self-stabilizing algorithm for information extraction from mobile wireless sensor networks

In the field of Wireless Sensor Networks (WSNs), a wide body of literature deals with Information Extraction (IE). The proposed schemes are based on the assumption that sensor nodes are static. There has been only a handful set of papers addressing IE in the presence of node mobility. The shortcomings of these approaches were discussed by the authors in a recent publication [1]. In this paper, we present a solution based on self-stabilization to enable seamless application of IE approaches designed for static networks to network with mobile nodes. We examine the general suitability of self-stabilization algorithms for WSNs. We describe the proposed algorithm and prove its correctness.

SCOUT: a sink camouflage and concealed data delivery paradigm for circumvention of sink-targeted cyber threats in wireless sensor networks

In modern epoch of cyber warfare and their countermeasures, wireless sensor networks (WSNs) are highly susceptible to cyber attacks due to their primary reliance over sink. WSNs perform routing and communication to deliver data from sources to sink. In this many-to-one communication paradigm, while some failure might be affordable at the many sources side, the single sink cannot be allowed any downtime, let alone be a failure. In a WSN security attack scenario, an attacker makes efforts to bring a sink down by identifying and capturing it. The current state of the art in sink protection schemes prevents such failures by preserving its privacy through letting it operate in promiscuous and all-the-time listening mode. However, such operation is still vulnerable to privacy divulgence because the attacker detects its all-the-time listening operation and identifies it. Furthermore, listening is an energy-expensive operation in WSNs that makes the sink battery die very quickly. In this paper, we propose a new sink privacy preservation scheme that defines the role of cooperating nodes. These cooperating nodes create a camouflage around the sink such that the location of the sink is never revealed. Such operational dispositioning reduces the susceptibility of WSNs generally and sink, particularly against the sink-targeted cyber attacks. Since the sink adopts sleep schedule, our scheme is energy efficient as well.