Coordination and resilience in wireless adhoc and sensor networks

Summary form only given. In wireless adhoc and sensor networks a close synergy and coordination is required among entities at different layers of the network architecture to achieve the robust behavior that is expected from these systems in the potentially harsh environments where they may operate. The volatile wireless channel, the unpredictability of traffic due to unknown traffic generation scenarios as well as variability of the network topology itself due to mobility and node failures set a challenging stage for the network designer. A mathematical network model that captures the interaction of mechanisms at the different layers, from physical to transport as well as the intricacies of the time varying network topology was considered in [1,2,3] and refined and generalized later in several other papers. The larger the capacity region is the better the performance will be since the network will be stable for a wide range of traffic loads and therefore more robust to traffic fluctuations. Such a performance criterion makes even more sense in the context of wireless ad-hoc and sensor networks where both the traffic load as well as the network capacity may vary unpredictably; in that case robustness is a valuable attribute. That perspective to the control of the network was introduced in [1]. The link capacity is allocated to the different traffic classes waiting for transmission through the link to the benefit of the traffic class with most unevenly distributed backlog. The distribution of the backlog build-up is an indication of the behavior of the system. A fluid model is considered in [4] where the information flow induced by the routing policy is represented by superflows. A superflow is a generalized notion of flow, where the aggregate incoming flow in a node may exceed the outgoing. The difference of incoming minus the outgoing flow from a node is the backlog buildup rate at the node. That difference is called “node overload”. The vector of n- de overloads under a certain routing policy is the quantitative performance objective that represents the overload response of the network to the routing policy. It is shown in [4] that in the space of node overload vectors there is one that is lexicographically minimal and is characterized. The overload corresponding to this vector also maximizes the information rate that reaches the sinks. Furthermore it is shown that this vector is the unique solution for a wide class of optimization problems where the optimization objective function is the sum of any non-decreasing convex function of node overloads. That vector is called “most balanced” overload vector and any superflow that induces the most balanced overload vector, “most balanced” superflow. A distributed adaptive superflow reallocation policy converging to a most balanced superflow is presented finally. That initial work sets the framework for studying the overload behavior of other wireless adhoc network architectures as well, towards more resilient wireless networks.