In wireless sensor-actor networks, sensors probe their surroundings and forward their data to actor nodes. Actors collaboratively respond to achieve predefined application mission. Since actors have to coordinate their operation, it is necessary to maintain a strongly connected network topology at all times. Moreover, the length of the inter-actor communication paths may be constrained to meet latency requirements. However, a failure of an actor may cause the network to partition into disjoint blocks and would, thus, violate such a connectivity goal. One of the effective recovery methodologies is to autonomously reposition a subset of the actor nodes to restore connectivity. Contemporary recovery schemes either impose high node relocation overhead or extend some of the inter-actor data paths. This paper overcomes these shortcomings and presents a Least-Disruptive topology Repair (LeDiR) algorithm. LeDiR relies on the local view of a node about the network to devise a recovery plan that relocates the least number of nodes and ensures that no path between any pair of nodes is extended. LeDiR is a localized and distributed algorithm that leverages existing route discovery activities in the network and imposes no additional prefailure communication overhead. The performance of LeDiR is analyzed mathematically and validated via extensive simulation experiments.

The pipeline leakage problem is a very challenging and critical issue. Solving this problem will save the nation a lot of money, resources and more importantly, it will save the environment. This paper discusses the state of the art of leak detection systems (LDSs) and data fusion approaches that are applicable to pipeline monitoring. A comparison of LDSs is performed based on well-defined criteria. We have classified and critically reviewed these techniques. A thorough analysis and comparison of all the recent works have been provided.

Demand-side management (DSM) plays a key role in the future of smart grids. Recently, DSM researchers have developed various mathematical models to optimize the demand response. Most of these works ignore the channel impairments' impact on the optimization process. In this paper, we propose a new noncooperative game theoretic model for the management of smart grid's demand considering the packet error rate in our formulation. We set the Nash equilibrium conditions for the proposed model. Under an assumption on the form of the utility functions, we develop a 0-1 mixed linear programming approach to compute nondominated extreme Nash equilibria. Results on a numerical example are provided and some useful insights are presented. Under some assumptions and a fully proven proposition, a feasible nondominated Nash equilibrium solution is found. Finally, we report and comment on computational experiments on randomly generated smart grid DSM game instances with different characteristics.

Cyber Physical Systems (CPS) such as power plants, water desalination utilities are just a few examples of systems that may come under stealth attacks. These attacks can threaten the proper operations of such systems without any indication. This problem necessitates the design of a control system that is able to work under such attacks. In this paper, an improved observer-based stabilising controller is proposed for CPS including random measurements and actuation delays and it is coming under distributed denial of service (DDoS) and deception attacks. The occurrences of DDoS and deception attacks are modelled as Bernoulli distributed white sequences with variable conditional probabilities. The criterion is presented in terms of linear matrix inequalities. Detailed simulation experiments on representative systems are shown to prove the applicability of the proposed methodology.