Route discovery based on energy-distance aware routing scheme for MANET

Route discovery proses in a Mobile Ad hoc Network (MANET) is challenging due to the limitation of energy at each network node. The energy constraint limits network connection lifetime thus affecting the routing process. Therefore, it is necessary for each node in the network to calculate routing factor in terms of energy and distance in deciding optimal candidate relay nodes needed to forward packets. This study proposes a new route discovery mechanism called the Energy-Distance Routing Aware (EDRA) that determines the selection of nodes during route discovery process to improve the network connection lifetime. This mechanism comprises of three schemes namely the Energy-Distance Factor Aware (EDFA), the Energy-Distance Forward Strategy (EDFS), and the Energy-Aware Route Selection (EARS). The EDFA scheme begins by calculating each nodes energy level (ei) and the distance (di) to the neighbouring nodes to produce the energy-distance factor value used in selecting the relay nodes. Next, the EDFS scheme forwards route request packets within discovery area of relay nodes based on the number of nodes. Then, the EARS scheme selects stable routing path utilising updated status information from EDFA and EDFS. The evaluation of EDRA mechanism is performed using network simulator Ns2 based on a defined set of performance metrics, scenarios and network scalability. The experimental results show that the EDRA gains significant improvement in the network connection lifetime when compared to those of the similar mechanisms, namely the AODV and the DREAM. EDRA also optimises energy consumption by utilising efficient forwarding decisions on varying scale of network nodes. Moreover, EDRA maximizes network connection lifetime while preserving throughput and packet drop ratio. This study contributes toward developing an efficient energy-aware routing to sustain longer network connection lifetime in MANET environment. The contribution is significant in promoting the use of green and sustainable next generation network technology

A complex-systems paradigm for modeling and solving finite large-scale discrete resources allocation problems – Part 1: a mathematical formalization of resources allocations problems

Allocating resources to achieve stated goals under defined constraints is a relevant problem in many industries. This problem is encountered in a variety of applications, including capacity planning in manufacturing, computer networks workload allocation, production planning, transports, logistics, capital budgeting, scheduling, timetabling, etc. Hence, finding good / optimal solutions to these problems has been the topic of intense research for several decades in the field of combinatorics and closely related areas of mathematics as well as in artificial intelligence and operational research. Depending on the application domain, the solutions might consist of arrangements, sequences, combinations, choices of objects, subsets, subgraphs, chains, routes in a network, assignments, schedules of jobs, packing schemes, etc. The two main categories of approaches to solving these problems are exact methods, that find solutions in a finite computational time, and heuristics, that have no guarantee for neither completeness nor optimality. However, heuristics are preferred when the problem might take too long or is computationally too intensive to be solved using exact methods. Over the years, many heuristics have been developed and successfully applied to model and solve the problem. Popular approaches to create such heuristics include: • Statistical analysis based on information derived from the problem’s data itself such as, for instance, the size distribution of the variables’ domain definition, the distribution of the number of variables in the constraints, etc. • Biological analogies based on structures and behaviors observed in living organisms such as, for instance, genetic pool recombination, swarm, slime molds growth, ant colony optimization, etc. • Physical analogies based on processes such as, for instance, simulated annealing in solid-state physics, local entropy reduction in thermodynamics, etc. However, to our knowledge, there is no general underlying theory to support these analogies and the heuristics that implement them. It is actually unclear why these heuristics actually work and their validation is mainly empirical. Our goal is to elaborate a general theory to model and solve large-scale discrete resources allocations problems. We intend to test the theory in developing and benchmarking a tool. The traditional approach in solving the problem is to consider separately on one hand the problem and on the other hand the heuristic to be applied to search for solutions. Our intuition is that to go beyond the empirical method, a paradigm shift is required in framing the problem and its resolution. In this research, we will explore a holistic approach to the problem and its resolution based on the concepts of the general system theory. The problem and its resolution method will be considered together as a whole, forming a complex system whose evolution converges towards the solutions