Aerial monitoring applications can be characterized by constantly changing operating conditions, and need for adequate resources to maintain stability, mobility and communication. Heterogeneous aerial sensor network has the ability to move nodes based on application specific goals that can provide three-dimensional sensing, and data distribution using hierarchical communication strategy. In this research paper, swarm based heterogeneous aerial sensor network is deployed on the fly using off-the shelf copters to provide increased sensing accuracy and reliability in comparison networks that require a prior knowledge of the infrastructure. The robustness of swarm approach makes it a suitable algorithm for aerial monitoring environment.
The amount of data processed annually over the Internet has crossed the zetabyte boundary, yet this Big Data
cannot be efficiently processed or stored using today's mobile devices. Parallel to this explosive growth in data, a
substantial increase in mobile compute-capability and the advances in cloud computing have brought the state-of-the-
art in mobile-cloud computing to an inflection point, where the right architecture may allow mobile devices to
run applications utilizing Big Data and intensive computing. In this paper, we propose the MObile Cloud-based
Hybrid Architecture (MOCHA), which formulates a solution to permit mobile-cloud computing applications such
as object recognition in the battlefield by introducing a mid-stage compute- and storage-layer, called the cloudlet.
MOCHA is built on the key observation that many mobile-cloud applications have the following characteristics:
1) they are compute-intensive, requiring the compute-power of a supercomputer, and 2) they use Big Data,
requiring a communications link to cloud-based database sources in near-real-time. In this paper, we describe
the operation of MOCHA in battlefield applications, by formulating the aforementioned mobile and cloudlet to
be housed within a soldier's vest and inside a military vehicle, respectively, and enabling access to the cloud
through high latency satellite links. We provide simulations using the traditional mobile-cloud approach as well
as utilizing MOCHA with a mid-stage cloudlet to quantify the utility of this architecture. We show that the
MOCHA platform for mobile-cloud computing promises a future for critical battlefield applications that access
Big Data, which is currently not possible using existing technology.
The future of metering networks requires adaptation of different sensor technology while reducing energy exploitation.
In this paper, a routing protocol with the ability to adapt and communicate reliably over varied IEEE standards is
proposed. Due to sensor's resource constraints, such as memory, energy, processing power an algorithm that balances
resources without compromising performance is preferred. The proposed A-PEARL protocol is tested under harsh simulated
scenarios such as sensor failure and fading conditions. The inherent features of A-PEARL protocol such as data
aggregation, fusion and channel hopping enables minimal resource consumption and secure communication.
Medical sensor network consist of heterogeneous nodes, wireless, mobile and wired with varied functionality. The
resources at each sensor require to be exploited minimally while sensitive information is sensed and communicated to
its access points using secure data mules. In this paper, we analyze the flat architecture, where different functionality
and priority information require varied resources forms a non-deterministic polynomial-time hard problem. Hence, a
bio-inspired data mule that helps to obtain dynamic multi-objective solution with minimal resource and secure path is
applied. The performance of the proposed approach is based on reduced latency, data delivery rate and resource cost.
In this paper, a swarm based ultra-wideband waveform and routing protocol is used for communicating messages in
the form of short pulses in sensor based health care application. Due to the time sensitivity of the application, a cognitive
protocol is applied to make decisions based on resource availability and quality-of-service. The combination of
swarm based physical and routing layer protocol helps in achieving an energy, bandwidth and time efficient application.
This paper compares the performance of cross layer protocol when exhaustive search and swarm based waveform
design is used.
Security in wireless sensor networks is typically sacrificed or kept minimal due to limited resources such as memory
and battery power. Hence, the sensor nodes are prone to Denial-of-service attacks and detecting the threats is crucial in
any application. In this paper, the Sybil attack is analyzed and a novel prediction method, combining Bayesian algorithm
and Swarm Intelligence (SI) is proposed. Bayesian Networks (BN) is used in representing and reasoning problems,
by modeling the elements of uncertainty. The decision from the BN is applied to SI forming an Hybrid
Intelligence Scheme (HIS) to re-route the information and disconnecting the malicious nodes in future routes. A performance
comparison based on the prediction using HIS vs. Ant System (AS) helps in prioritizing applications where decisions
Sensors have varied constraints, which makes the network challenging for communicating with its peers. In this
paper, an extension to the security of physical layer of a predictive sensor network model using the ant system is proposed.
The Denial of Service (DoS) attack on sensor networks not only diminishes the network performance but also
affects the reliability of the information making detection of a DoS threat is more crucial than recovering from the
attack. Hence, in this paper, a novel approach in detecting the DoS attack is introduced and analyzed for a variety of
scenarios. The DoS attack is dependent on the vulnerabilities in each layer, with the physical layer being the lowest
layer and the first to be attacked by jammers. In this paper, the physical layer DoS attack is analyzed and a defense
mechanism is proposed. Classification of the jammer under various attack scenarios is formulated to predict the
genunity of the DoS attacks on the sensor nodes using receiver operating characteristics (ROC). This novel approach
helps in achieving maximum reliability on DoS claims improving the Quality of Service (QoS) of WSN.
Sensors have varied constraints, which make the network challenging for communicating with peers. In this paper, an extension, to the physical layer of the previous predictive sensor network model using the ant system is proposed. The tiny and low-cost sensor nodes are made of RF wireless links, where the states of the nodes vary with respect to time and environment. The ant system is a learning algorithm, that can be used to solve any NP hard communication problem and possesses characteristics such as robustness and versatility. The ant system possesses unique features that keep the network functional by detecting weak links and re-routing the agents. The swarm agents are distributed along the network, where the agent communicates with its neighbors (agents) by means of pheromone deposition and tabu list. The transition probability in the ant system includes an objective function, which is influenced by the poset weights. The poset weights on each of the orthogonal communication parameters greatly affects the decisions made by ant system. The agents carry updated information of its previous nodes, which helps in monitoring the strength of the communication links.
Through simulation, comparison between DSSS-BPSK and Bluetooth-GFSK signals are shown. This paper demonstrates the robustness of the model under slow/fast fading, and energy loss at node during transmission. Implementation of this algorithm should be able to handle hostile environmental conditions and human tampering of data. The performance of the network is evaluated based on accuracy and response time of the agents within the network.
The need for a robust predictive sensor communication network inspired this research. There are many critical issues in a communication network with different data rate requirements, limited power and bandwidth. Energy consumption is one of the key issues in a sensor network as energy dissipation occurs during routing, communication and monitoring of the environment. This paper covers the routing of a sensor communication network by applying an evolutionary algorithm -- the ant system. The issues considered include optimal energy, data fusion from different sensor types and predicting changes in environment with respect to time.