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Research

Events

IEEE ISCC 2017 5th International Workshop on Smart City and Ubiquitous Computing Applications (SCUCA 2017), 3 July 2017, Heraklion, Crete, Greece.

News

Mr. Rafay Iqbal Ansari, PhD Candidate, after being selected and invited by the IEEE Communications Society, will participate to the 2017 IEEE ComSoc Summer School to be held in University of New Mexico, Albuquerque, NM, USA, from July 17 to July 20, 2017 [Read more]

Mr. Rafay Iqbal Ansari, PhD Candidate, was nominated by CYNET for the IT Studentsí Lightning Talk Challenge, as part of the GEANT Future Talent Fund (FTF) initiatives, that took place in Linz, Austria, 29 May - 2 June 2017 [Read more]

Our PhD Candidate Rafay Iqbal Ansari, member of the team "SherPak", won the third place in Ericsson Innovation Awards 2016, selected among 843 teams worldwide, with "SmartWindows Communication" project idea [Read more]

Admission to the Ph.D. program in Computer Engineering (Networks concentration) [Read more]

Admission to the Ph.D. program in Computer Science (Networks concentration) [Read more]

Admission to the M.Sc. program in Electrical Engineering (Computer Engineering specialization - Networks concentration) [Read more]

Admission to the M.Sc. program in Web and Smart Systems (Smart Systems concentration) [Read more]

Frederick CISCO Academy CCNA course starts in October 2017 [Read more]

CISCO Networking Academy NetRiders CCNA 20174 Competition [Read more]

Cisco Networking Academy Evolution: New CCNA Routing and Switching course [Read more]

SENSOR NETWORKS

Mobility Management in WSNs

The focus is on the development of a novel, intelligent controller to support mobility in wireless sensor networks (WSNs). Particularly, the deployment of such mobility solution is in critical applications, like personnel safety in an industrial environment. A fuzzy logic-based mobility controller is proposed to aid sensor Mobile Nodes (MN) to decide whether they have to trigger the handoff procedure and perform the handoff to a new connection position or not. To do so, we use a combination of two locally available metrics, the RSSI and the Link Loss, in order to “predict" the End-to-End losses and support the handoff triggering procedure. As a performance evaluation environment, a real industrial setting (oil refinery) is used. Based on onsite experiments run in the oil refinery testbed area, the proposed mobility controller has shown significant benefits compared to other conventional solutions, in terms of packet loss, packet delivery delay, energy consumption, and ratio of successful handoff triggers. Further, we have evaluated the stability and boundedness of our system using phase plane analysis, with supportive results.

The uniqueness of this work is threefold. Firstly, an intelligent controller, based on fuzzy logic is proposed. Secondly, a real industrial setting (oil refinery) is used as the evaluation environment, something that poses new challenges regarding the design of mobility support. Thirdly, the approach taken has greater applicability to any WSN industrial environment or testbed setting with mobility requirements, due to the fact that it is designed based on network state parameters that are available to all sensor MNs. The selection of fuzzy logic system is based on its simplicity and the fact that since it processes experts-defined rules governing the target control system, it can be modified to improve system performance.

Multi-objective Deployment and Power Assignment in Wireless Sensor Networks using Metaheuristics

A WSN design often requires the decision of optimal locations (deployment) and transmit power levels (power assignment) for a number of sensors to be deployed in an area of interest. Few attempts have been made at optimizing both decision variables for maximizing the network coverage and lifetime objectives under connectivity/fault tolerance constraints. Even though, most of the studies consider the two objectives individually. This often results in ignoring and losing "better" solutions, since WSN coverage and lifetime are conflicting objectives and a Decision Maker (DM) needs an optimal trade-off. In this work, the Deployment and Power Assignment Problem (DPAP) is defined and formulated as two Multiobjective Optimization Problems (MOPs), i.e. DPAP1 and DPAP2. Multi-Objective Evolutionary Algorithms (MOEAs) are very efficient and effective for dealing with MOPs. Most of the WSN MOPs in the literature are tackled with generic MOEAs due to their ease of implementation and wide adaptability. Generic MOEAs tackle aMOP without using problem-specific knowledge. This could have undesirable effects on their performance when dealing with problems such as DPAPs. The incorporation of problem-specific knowledge in MOEAs can be proven useful. However, designing problem-specific EA operators for a MOP as a whole is often difficult. The Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) alleviates this difficulty by decomposing the MOP into a set of scalar subproblems, which are optimized in parallel using neighbourhood information and scalar techniques. The difficulty of adding knowledge in MOEA/D is that the subproblems have different objective preferences, require different treatment and have to be solved in a single run. Therefore, the problem-specific operators should adapt to the requirements of each subproblem dynamically during the evolution. In this thesis, we propose a problemspecific MOEA/D, which incorporates WSN knowledge in almost every part of its general-purpose framework to effectively and efficiently tackle the DPAPs in WSNs.

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