MANETs will play a critical role for communication in future battlefields. However, wireless communications, especially MANETs are susceptible to jamming. While MANETs will adapt to continuous jamming, intermittent jamming can disrupt the routing protocol. In the intermittent case, the network topology during non-jamming is generally quite different from the one during jamming. In the case of jamming by the enemy, one can use physical means to stop the jamming. However, in recent conflicts, jamming by friendly forces has been widely used. In this paper, we propose the jamming-aware routing protocols for the friendly jamming scenario where the jamming schedule (i.e., when jamming is to occur) is known to the friendly mobile nodes, but hidden from all enemy mobile nodes. The key idea is that the routing protocol freezes during jamming, whereas it resumes during non-jamming. This simple, but very effective, algorithm significantly improves the packet delivery ratio and other metrics compared to other routing protocols where the categories of the routing protocols considered are proactive and unicast with single-path or multipath. Finally, this paper concludes with a discussion on a routing protocol, which combines game-theoretic routing with jamming detection and probabilistic forwarding to avoid and overcome malicious jamming.

We propose a Context-aware Smallworld routing protocol for wireless ad-hoc networks which finds efficient routes to the destination nodes often through a very short chain of intermediate nodes, without using any routing table. This protocol exploits the “small-world” phenomenon of social networks where source-destination pairs typically get connected through few intermediate friends and the individuals can collectively discover such short paths. The key idea behind our routing protocol is that for forwarding an incoming packet to the destination, a node picks the next hop from among the one-hop neighbor from its one-hop neighborhood that is closest to the destination, and multiple randomly selected long-distance neighbor(s), i.e., from outside of its one-hop neighborhood, based on which of these neighbors is closest to the destination. The long-distance neighbors are selected by using their contextual information through a probabilistic mapping. We provide the theoretical foundations behind our protocol. This simple but effective algorithm can meet performance objectives for most scenarios by reducing loss and latency. Simulation results, using synthetic network topologies, have demonstrated that the routing performance of context-aware Smallworld is better than geographical (distance-based) Smallworld and other standard proactive routing protocols (e.g., OLSRv2) when metrics, such as packet losses, end-to-end delays, hop-counts, connectivity drops, and control traffic generated, are used as the key performance indicators. We also show that the proposed protocol is resilient to dynamic topology changes.

Software release management is the process of determining, acquiring, releasing and deploying changes into an information technology (IT) environment. The process has often been manual to a certain extent. However, the process can become quite complicated and unmanageable for communications networks, where the managed service providers may need to serve and upgrade multiple customers, environments, networks, devices and components - in parallel. An important design goal of such a process is to have a distributed, integrated and self-managed system that can continuously monitor the networks and devices, detect and determine the exceptions, locate and retrieve the upgrades from their respective sources, release and deploy the upgrades in the affected environment; all in a timely and a non-disruptive manner. This paper introduces the problem of software release management for communications networks, proposes an agent model and a novel protocol for autonomic software release management, and discusses a prototype autonomic software release management system that can be effectively used by the service providers to upgrade and maintain their customers' networks.

In this paper, we present a multi-layered, middleware-driven, multi-agent, interoperable architecture for distributed sensor networks, that bridges the gap between the programmable application layer consisting of software agents and the physical layer consisting of extremely small, low power devices that combine programmable computing with sensing, tracking and wireless communication capabilities. We develop an energy-efficient approach for collaborative processing among multiple sensor nodes using a mobile-agent-based computing model. Unlike the traditional client/server-based model where each sensor node sends data to the sink, in this model the sink/base-station deploys mobile agents that migrate from node to node following a certain itinerary and fuses the information/data locally at each node. This way, the intelligence is distributed throughout the network edge and communication cost is reduced to make the sensor network energy-efficient. Simulation results show that the mobile-agent-based approach is advantageous for large distributed sensor network environment

Self-organization is critical for a distributed wireless sensor network due to the spontaneous and random deployment of a large number of sensor nodes over a remote area. Such a network is often characterized by its abilities to form an organizational structure without much centralized intervention. An important design goal for a smart sensor network is to be able have an energy-efficient, self-organized configuration of sensor nodes that can scan, detect, and track targets of interest in a distributed manner. In this paper, we propose a novel self-organization protocol and describe other relevant, indigenous building blocks that can be combined to build integrated surveillance applications for self-organized sensor networks. Experiments in both simulated and real-world platforms indicate that this protocol can be useful for tracking targets that follow a predictable course

We describe a distributed sensor network test-bed and a surveillance experiment to demonstrate the integration of distributed tracking algorithms with strategies for location estimation, energy management and mobility management of sensor nodes. The test-bed consists of real sensor nodes augmented with a simulated environment. Data from real world tracking is provided to the simulated environment, where it is used to self-organize the sensor network in an energy-efficient way. Results from the simulation are then fed back to the real world to enable the sensor network to reorganize for reinforced tracking.

In this paper, we argue that the standard-based intelligent agents constitute an attractive technology to address the challenging issues arising from the unpredictable nature and dynamism of modern heterogeneous networks. Paramount among these issues are diverse services, networks and technologies, multiple vendor equipment, loosely organized management applications and inconsistent databases, multiple management protocols and multiple data representations.