The drive towards a modern, efficient and information-driven grid - the "Smart Grid" - necessitates the incorporation of computationally intelligent infrastructure. For instance, residential load management strategies may require the scheduling of appliances in order to achieve certain objectives such as load factor maximization/peak-to-average (PAR) ratio minimization or minimization of energy cost. This paper presents an approach to one of such load scheduling problem, which involves the use of the metaheuristic optimization technique that is Genetic Algorithms (GA). We consider a scenario in which dynamic pricing is adopted and the objective is to minimize the overall cost of electricity payment while satisfying a set of constraints. MATLAB was used as the simulation platform and results confirm that Genetic Algorithm can optimize energy consumption over a set of constraints we have defined, thus minimizing overall electricity cost for the Nigerian consumer in a smart pricing environment.

Wireless sensor networks (WSNs) have become one of the current research areas, and it proves to be a very supportive technology for various applications such as environmental-, military-, health-, home-, and office-based applications. WSN can either be mobile wireless sensor network (MWSN) or static wireless sensor network (SWSN). MWSN is a specialized wireless network consisting of considerable number of mobile sensors, however the instability of its topology introduces several performance issues during data routing. SWSNs consisting of static nodes with static topology also share some of the security challenges of MWSNs due to some constraints associated with the sensor nodes. Security, privacy, computation and energy constraints, and reliability issues are the major challenges facing WSNs, especially during routing. To solve these challenges, WSN routing protocols must ensure confidentiality, integrity, privacy preservation, and reliability in the network. Thus, efficient and energy-aware countermeasures have to be designed to prevent intrusion in the network. In this chapter, we describe different forms of WSNs, challenges, solutions, and a point-to-point multi-hop-based secure solution for effective routing in WSNs.