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We design a self-stabilizing cluster routing algorithm based on the link-cluster architecture of wireless ad hoc networks. The network is divided into clusters. Each cluster has a single special node, called clusterhead that contains the routing information about inter and intra-cluster communication. The proposed algorithm assumes that all nodes have unique IDs. The algorithm achieves two tasks. First, the set of special nodes (clusterheads) is elected such that it models the link-cluster architecture: any node belongs to a single cluster, is within two hops of the clusterhead, and knows the direct neighbor on the shortest path toward the clusterhead. Second, the routing tables are maintained by the clusterheads to store information about nodes both within and outside the cluster. There are two advantages of maintaining routing tables only in the clusterheads. First, as no two neighboring nodes are clusterhead (as per the link-cluster architecture), there exists no consistency problems. Second, since other nodes are responsible for forwarding only, they use less power. So, when the CH runs out of power, some neighboring node will be available to take on the task.A self-stabilizing system [3] has the ability to automatically recover to normal behavior in case of transient faults, without a centralized control. The MANET can start in some arbitrary state and with no knowledge of the network topology, but still eventually selects a set of clusterhead nodes (as specified by the link-cluster architecture) in a constant amount of time (¾´Ø Ñ Ô Ö Ó · ¾ µ · Ò´Ø Ñ Ô Ö Ó · ½ µ ¾ time units, where Ò represents the total number of nodes in MANET). Then in these special nodes, routing tables are built with information about shortest paths for intra-cluster routing and shortest paths for inter-cluster routing (based on on-demand set of nodes).

The most common approach for border patrol operations is the use of human personnel and manned ground vehicles, which is expensive, at times inefficient and sometimes even hazardous to people involved. A better approach would be using Unmanned Aerial Vehicles (UAVs) in combination with such ground sensors. This would help improve the overall effectiveness of the surveillance system as a UAV could first scan the alert area before sending in personnel and vehicles, if deemed necessary. We propose border surveillance using multiple Unmanned Aerial Vehicles (UAVs) in combination with alert stations consisting of Unattended Ground Sensors (UGSs) along the border line/fence. Upon detecting an event, an alert would be triggered by any UGS. We simulate this process by reading probability data for different timestamps from a text file. And, based on utility values of each station, two UAVs decide on which alert station to service.

Unmanned systems, with and without a human-in-the loop, are being deployed in a range of military and civilian applications spanning air, ground, sea-surface and undersea environments. Large investments, particularly in robotics, electronic miniaturization, sensors, network communication, information technology and artificial intelligence are likely to further accelerate this trend. The operation of unmanned systems, and of applications that use these systems, are heavily dependent on cyber systems that are used to collect, store, process and communicate data, making data a critical resource. At the same time, undesirable elements of our society and adversarial states have also realized the high value of this resource. While enormous efforts have been made to secure data and cyber systems, lack of rigorous threat modeling and risk analysis can lead to more specific, rather than generic, security solutions relevant to the cyber system to be protected. This scenario has created an urgent need to develop a holistic process for protecting data and cyber systems. This paper deals with the development of different pieces of this process. We first identify the security requirements of unmanned autonomous systems, and follow this up with modeling how attacks achieve their objectives. We argue that a large number of threats that can materialize as attacks and the costs of managing these attacks in cost effective ways require ranking threats using cyber threat modeling and cyber risk analysis techniques. The last segment of the paper describes a structured approach to mitigate high-risk threats.

We study the problem of allocating memory of servers in a data center based on online requests for storage. Given an online sequence of storage requests and a cost associated with serving the request by allocating space on a certain server one seeks to select the minimum number of servers as to minimize total cost. We use two different algorithms and propose a third algorithm. We show that our proposed algorithm performs better for large number of random requests in terms of the variance in the average number of servers.