Alan D. Amis

Bio: Alan D. Amis is an academic researcher from Rockwell Collins. The author has contributed to research in topics: Node (networking) & Ring network. The author has an hindex of 7, co-authored 15 publications receiving 1415 citations.

Max-min d-cluster formation in wireless ad hoc networks

Abstract: An ad hoc network may be logically represented as a set of clusters. The clusterheads form a d-hop dominating set. Each node is at most d hops from a clusterhead. Clusterheads form a virtual backbone and may be used to route packets for nodes in their cluster. Previous heuristics restricted themselves to 1-hop clusters. We show that the minimum d-hop dominating set problem is NP-complete. Then we present a heuristic to form d-clusters in a wireless ad hoc network. Nodes are assumed to have a non-deterministic mobility pattern. Clusters are formed by diffusing node identities along the wireless links. When the heuristic terminates, a node either becomes a clusterhead, or is at most d wireless hops away from its clusterhead. The value of d is a parameter of the heuristic. The heuristic can be run either at regular intervals, or whenever the network configuration changes. One of the features of the heuristic is that it tends to re-elect existing clusterheads even when the network configuration changes. This helps to reduce the communication overheads during transition from old clusterheads to new clusterheads. Also, there is a tendency to evenly distribute the mobile nodes among the clusterheads, and evently distribute the responsibility of acting as clusterheads among all nodes. Thus, the heuristic is fair and stable. Simulation experiments demonstrate that the proposed heuristic is better than the two earlier heuristics, namely the LCA and degree-based solutions.

Load-balancing clusters in wireless ad hoc networks

Abstract: Ad hoc networks consist of a set of identical nodes that move freely and independently and communicate with other node via wireless links. Such networks may be logically represented as a set of clusters by grouping together nodes that are in close proximity with one another. Clusterheads form a virtual backbone and may be used to route packets for nodes in their cluster. Nodes are assumed to have non-deterministic mobility pattern. Clusters are formed by diffusing node identities along the wireless links. Different heuristics employ different policies to elect clusterheads. Several of these policies are biased in favor of some nodes. As a result, these nodes shoulder greater responsibility and may deplete their energy faster, causing them to drop out of the network. Therefore, there is a need for load-balancing among clusterheads to allow all nodes the opportunity to serve as a clusterhead. We propose a load-balancing heuristic to extend the life of a clusterhead to the maximum budget before allowing the clusterhead to retire and give way to another node. This helps to evenly distribute the responsibility of acting as clusterheads among all nodes. Thus, the heuristic ensures fairness and stability. Simulation experiments demonstrate that the proposed heuristic does provide longer clusterhead durations than with no load-balancing.

Distance vector routing via multi-point relays

Abstract: The present invention is a method and system for propagating routing information in a wireless network. In an exemplary embodiment, the method includes defining a set of artery nodes to propagate distance vector routing information throughout the wireless network. The set of artery nodes may broadcast distance vector routing information to neighboring network nodes. The method may also include receiving distance vector routing information by the neighboring nodes to modify distance vector routing tables defined within each of the neighboring nodes. The set of artery nodes may then re-broadcast distance vector routing information in order to propagate distance vector routing information throughout the wireless network.

Scalable mobile adaptive reliable ToS based automatic retransmit request

Abstract: A method for handling data transmission errors in a wireless communication network includes receiving a first data from a first node at a second node using a data transmission protocol having a first slot for transmitting the first data. The first slot includes a plurality of bits and a first slot header comprising an acknowledgment request for at least one group of bits. The method also includes evaluating the first set of data for errors and generating a second slot using the data transmission protocol. The second slot includes at least a second slot header comprising acknowledgement data in response to the acknowledgement request. The acknowledgement data has a structure based on a Forward Error Correction (FEC) protocol block size. The second slot is then transmitted to the first node.

Inter-channel bridge node communications protocol for TDMA networks

Abstract: An improved protocol for MANETs used in TDRS, JTRS systems and the like, where multiple channels have multiple communication links operating simultaneously within range of each other. All nodes in such MANETs are able to communicate with all their “1 hop” neighbors, even if the neighbors are on different channels. An improved inter-channel communication protocol using a bridge node allows nodes on separate channels to communicate. The bridge node operates as a pseudo-bi-directional link between two neighboring channels in the MANET. An Inter-Channel Bridge Node Communication Protocol determines contention free time slots on both channels, and then reserves one time slot to be used for inter-channel communications. The reserved time slot usage is then divided between the transmitted bridge node and the receiving node in the neighboring channel.

HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

Abstract: Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. We propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.

Protocols for self-organization of a wireless sensor network

Abstract: We present a suite of algorithms for self-organization of wireless sensor networks in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes.

An energy efficient hierarchical clustering algorithm for wireless sensor networks

Abstract: A wireless network consisting of a large number of small sensors with low-power transceivers can be an effective tool for gathering data in a variety of environments. The data collected by each sensor is communicated through the network to a single processing center that uses all reported data to determine characteristics of the environment or detect an event. The communication or message passing process must be designed to conserve the limited energy resources of the sensors. Clustering sensors into groups, so that sensors communicate information only to clusterheads and then the clusterheads communicate the aggregated information to the processing center, may save energy. In this paper, we propose a distributed, randomized clustering algorithm to organize the sensors in a wireless sensor network into clusters. We then extend this algorithm to generate a hierarchy of clusterheads and observe that the energy savings increase with the number of levels in the hierarchy. Results in stochastic geometry are used to derive solutions for the values of parameters of our algorithm that minimize the total energy spent in the network when all sensors report data through the clusterheads to the processing center.

WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks

Abstract: In this paper, we propose an on-demand distributed clustering algorithm for multi-hop packet radio networks. These types of networks, also known as i>ad hoc networks, are dynamic in nature due to the mobility of nodes. The association and dissociation of nodes to and from i>clusters perturb the stability of the network topology, and hence a reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The i>clusterheads, form a i>dominant set in the network, determine the topology and its stability. The proposed weight-based distributed clustering algorithm takes into consideration the ideal degree, transmission power, mobility, and battery power of mobile nodes. The time required to identify the clusterheads depends on the diameter of the underlying graph. We try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control (MAC) protocol. The non-periodic procedure for clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. The clusterheads, operating in “dual" power mode, connects the clusters which help in routing messages from a node to any other node. We observe a trade-off between the uniformity of the load handled by the clusterheads and the connectivity of the network. Simulation experiments are conducted to evaluate the performance of our algorithm in terms of the number of clusterheads, i>reaffiliation frequency, and dominant set updates. Results show that our algorithm performs better than existing ones and is also tunable to different kinds of network conditions.