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PREFACENumerous people have supported us during the development of this dissertation and our undergraduate experience more generally. A few words' mention here cannot adequately capture all our appreciation.We are very thankful to our thesis coordinator Dr. ABSTRACTLong term evolution (LTE) network, incompatible with 2G and 3G networks is the most promising technology for wireless communication with higher speed and capacity. Selforganized load balancing is an important research issue for the wireless networks. Game theory provides an efficient way to provide self-organizing properties in a distributed environment like LTE networks. Load balancing means to assign users from highly loaded cells to neighbor lower loaded cells. The amount of load needs to be offloaded or accepted by a particular cell is not really specified and currently totally vendor specified. In our proposed cooperative game theoretic approach, each cell is considered as a player where they trade the load by forming a coalition by satisfying the overall performance of the network. Simulation results show that our proposed method provides better performance in terms of satisfied users and adjusted load values.

One of the challenging issues in a distributed computing system is to reach on a decision with the presence of so many faulty nodes. These faulty nodes may update the wrong information, provide misleading results and may be nodes with the depleted battery power. Consensus algorithms help to reach on a decision even with the faulty nodes. Every correct node decides some values by a consensus algorithm. If all correct nodes propose the same value, then all the nodes decide on that. Every correct node must agree on the same value. Faulty nodes do not reach on the decision that correct nodes agreed on. Binary consensus algorithm and average consensus algorithm are the most widely used consensus algorithm in a distributed system. We apply binary consensus and average consensus algorithm in a distributed sensor network with the presence of some faulty nodes. We evaluate these algorithms for better convergence rate and error rate.