Iordanis Koutsopoulos

Optimal Jamming Attacks and Network Defense Policies in Wireless Sensor Networks

We consider a scenario where a sophisticated jammer jams an area in a single-channel wireless sensor network. The jammer controls the probability of jamming and transmission range to cause maximal damage to the network in terms of corrupted communication links. The jammer action ceases when it is detected by a monitoring node in the network, and a notification message is transferred out of the jamming region. The jammer is detected at a monitor node by employing an optimal detection test based on the percentage of incurred collisions. On the other hand, the network computes channel access probability in an effort to minimize the jamming detection plus notification time. In order for the jammer to optimize its benefit, it needs to know the network channel access probability and number of neighbors of the monitor node. Accordingly, the network needs to know the jamming probability of the jammer. We study the idealized case of perfect knowledge by both the jammer and the network about the strategy of one another, and the case where the jammer or the network lack this knowledge. The latter is captured by formulating and solving optimization problems, the solutions of which constitute best responses of the attacker or the network to the worst-case strategy of each other. We also take into account potential energy constraints of the jammer and the network. We extend the problem to the case of multiple observers and adaptable jamming transmission range and propose a intuitive heuristic jamming strategy for that case.

Optimal incentive-driven design of participatory sensing systems

Participatory sensing has emerged as a novel paradigm for data collection and collective knowledge formation about a state or condition of interest, sometimes linked to a geographic area. In this paper, we address the problem of incentive mechanism design for data contributors for participatory sensing applications. The service provider receives service queries in an area from service requesters and initiates an auction for user participation. Upon request, each user reports its perceived cost per unit of amount of participation, which essentially maps to a requested amount of compensation for participation. The participation cost quantifies the dissatisfaction caused to user due to participation. This cost is considered to be private information for each device, as it strongly depends on various factors inherent to it, such as the energy cost for sensing, data processing and transmission to the closest point of wireless access, the residual battery level, the number of concurrent jobs at the device processor, the required bandwidth to transmit data and the related charges of the mobile network operator, or even the user discomfort due to manual effort to submit data. Hence, participants have strong motive to mis-report their cost, i.e. declare a higher cost that the actual one, so as to obtain higher payment. We seek a mechanism for user participation level determination and payment allocation which is most viable for the provider, that is, it minimizes the total cost of compensating participants, while delivering a certain quality of experience to service requesters. We cast the problem in the context of optimal reverse auction design, and we show how the different quality of submitted information by participants can be tracked by the service provider and used in the participation level and payment selection procedures. We derive a mechanism that optimally solves the problem above, and at the same time it is individually rational (i.e., it motivates users to participate) and incentive-compatible (i.e. it motivates truthful cost reporting by participants). Finally, a representative participatory sensing case study involving parameter estimation is presented, which exemplifies the incentive mechanism above.

The Role of Aggregators in Smart Grid Demand Response Markets

The design of efficient Demand Response (DR) mechanisms for the residential sector entails significant challenges, due to the large number of home users and the negligible impact of each of them on the market. In this paper, we introduce a hierarchical market model for the smart grid where a set of competing aggregators act as intermediaries between the utility operator and the home users. The operator seeks to minimize the smart grid operational cost and offers rewards to aggregators toward this goal. Profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users in order to modify their preferable consumption pattern. Finally, end-users seek to optimize the tradeoff between earnings received from the aggregator and discomfort from having to modify their pattern. Based on this market model, we first address the benchmark scenario from the point of view of a cost-minimizing operator that has full information about user demands. Then, we consider a DR market, where all entities are self-interested and non-cooperative. The proposed market scheme captures the diverse objectives of the involved entities and, compared to flat pricing, guarantees significant benefits for each. Using realistic demand traces, we quantify the arising DR benefits. Interestingly, users that are extremely willing to modify their consumption pattern do not derive maximum benefit.

A framework for MAC protocol misbehavior detection in wireless networks

The pervasiveness of wireless devices and the architectural organization of wireless networks in distributed communities, where no notion of trust can be assumed, are the main reasons for the growing interest in the issue of compliance to protocol rules. Reliable and timely detection of deviation from legitimate protocol operation is recognized as a prerequisite for ensuring efficient and fair use of network resources and minimizing performance losses. Nevertheless, the random nature of protocol operation together with the inherent difficulty of monitoring in the open and highly volatile wireless medium poses significant challenges. In this paper, we consider the fundamental problem of detection of node misbehavior at the MAC layer. Starting from a model where the behavior of a node is observable, we cast the problem within a minimax robust detection framework, with the objective to provide a detection rule of optimum performance for the worst-case attack. The performance is measured in terms of required number of observations in order to derive a decision. This framework is meaningful for studying misbehavior because it captures the presence of uncertainty of attacks and concentrates on the attacks that are most significant in terms of incurred performance losses. It also refers to the case of an intelligent attacker that can adapt its policy to avoid being detected. Although the basic model does not include interference, we show that our ideas can be extended to the case where observations are hindered by interference due to concurrent transmissions. We also present some hints for the problem of notifying the rest of the network about a misbehavior event. Our work provides interesting insights and performance bounds and serves as a prelude to a future study that would capture more composite instances of the problem.

Optimal energy storage control policies for the smart power grid

Electric energy storage devices are prime candidates for demand load management in the smart power grid. In this work, we address the optimal energy storage control problem from the side of the utility operator. The operator controller receives power demand requests with different power requirements and durations that are activated immediately. The controller has access to one energy storage device of finite capacity. The objective is to devise an energy storage control policy that minimizes long-term average grid operational cost. The cost is a convex function of instantaneous power demand that is satisfied from the grid, and it reflects the fact that each additional unit of power needed to serve demands is more expensive as the demand load increases. For the online dynamic control problem, we derive a threshold-based control policy that attempts to maintain balanced power consumption from the grid at all times, in the presence of continual generation and completion of demands. The policy adaptively performs charging or discharging of the storage device. The former increases power consumption from the grid and the latter satisfies part of the grid demand from the stored energy. We prove that the policy is asymptotically optimal as the storage capacity becomes large, and we numerically show that it performs very well even for finite capacity. The off-line problem over a finite time horizon that assumes a priori known power consumption to be satisfied at all times, is formulated and solved with Dynamic Programming. Finally, we show that the model, approach and structure of the optimal policy can be extended to also account for a renewable source that feeds the storage device.

Adaptive resource allocation in SDMA-based wireless broadband networks with OFDM signaling

The increasing popularity of wireless broadband access in local and wide area networks is the main expression of the need for flexible and ubiquitous wireless connectivity. In order to satisfy user resource requirements in the presence of volatility of the wireless medium, sophisticated multiple access and adaptation techniques are required, which alleviate channel impairments and increase system throughput. The use of multiple antennas at the base station allows intra-cell channel reuse by multiple spatially separable users through space division multiple access (SDMA) and hence enhances cell capacity. However, the employment of antennas in the physical layer raises significant issues in the medium access control (MAC) layer. We investigate the impact of antenna arrays on MAC layer channel allocation in the context of orthogonal frequency division multiplexing (OFDM), which is the predominantly proposed signaling scheme for wireless broadband access. We propose an algorithm to allocate channels to users based on their spatial separability properties, while appropriately adjusting beamforming weights and transmission rates for each user in a channel. The unified consideration of such adaptive techniques yields significant throughput benefits.

Optimal Jamming Attack Strategies and Network Defense Policies in Wireless Sensor Networks

We consider a scenario where a sophisticated jammer jams an area in which a single-channel random-access-based wireless sensor network operates. The jammer controls the probability of jamming and the transmission range in order to cause maximal damage to the network in terms of corrupted communication links. The jammer action ceases when it is detected by the network (namely by a monitoring node), and a notification message is transferred out of the jammed region. The jammer is detected by employing an optimal detection test based on the percentage of incurred collisions. On the other hand, the network defends itself by computing the channel access probability to minimize the jamming detection plus notification time. The necessary knowledge of the jammer in order to optimize its benefit consists of knowledge about the network channel access probability and the number of neighbors of the monitor node. Accordingly, the network needs to know the jamming probability of the jammer. We study the idealized case of perfect knowledge by both the jammer and the network about the strategy of each other and the case where the jammer and the network lack this knowledge. The latter is captured by formulating and solving optimization problems where the attacker and the network respond optimally to the worst-case or the average-case strategies of the other party. We also take into account potential energy constraints of the jammer and the network. We extend the problem to the case of multiple observers and adaptable jamming transmission range and propose a meaningful heuristic algorithm for an efficient jamming strategy. Our results provide valuable insights about the structure of the jamming problem and associated defense mechanisms and demonstrate the impact of knowledge as well as adoption of sophisticated strategies on achieving desirable performance.

Control and optimization meet the smart power grid: scheduling of power demands for optimal energy management

The smart power grid harnesses information and communication technologies to enhance reliability and enforce sensible use of energy through effective management of demand load. We envision a scenario with real-time communication between the grid operator and the consumers. The operator controller receives consumer power demand requests with different power requirements, durations, and deadlines by which they are to be activated. The objective of the operator is to devise a power demand task scheduling policy that minimizes the grid operational cost over a time horizon. The cost is a convex function of total instantaneous power consumption and reflects the fact that each additional unit of power needed to serve demands is more expensive as the demand load increases. First, we study the off-line demand scheduling problem, where parameters are known a priori. If demands can be scheduled preemptively, the problem is a load balancing one, and we present an iterative algorithm that optimally solves it. If demands need to be scheduled non-preemptively, the problem is a bin packing one. Next, we devise a stochastic model for the case when demands are generated continually and scheduling decisions are taken online, and we focus on long-term average cost. We present two types of demand load control based on current power consumption. In the first one, the controller may choose to serve a new demand request upon arrival or postpone it to the end of its deadline. The second one, termed Controlled Release (CR) activates a new request if the current power consumption is less than a threshold, otherwise the demand is queued. Queued demands are activated when their deadlines expire, or if consumption drops below the threshold. We derive a lower performance bound for all policies, which is asymptotically achieved by the CR policy as deadlines increase. For both types above, optimal policies are of threshold nature. Numerical results validate the benefit of our approaches compared to the default policy of serving demands upon arrival.

Challenges in demand load control for the smart grid

The ground to address challenges in demand load control for the smart power grid is now more fertile than ever, due to advances in communication infrastructures and the creation of a two-way channel for real-time communication between consumers and the utility operator. After giving a taxonomy of methods for demand load control, we focus on two of these methods that aim at minimizing the grid operational cost. The cost is a convex function of instantaneous power consumption and reflects the fact that each additional unit of power needed to serve demands is more expensive as the demand load increases. First, we consider online scheduling of power demand tasks that have time flexibility in being activated, in terms of a deadline. We outline the rationale for threshold-based policies that activate a demand if the instantaneous consumption is low; otherwise, they postpone it until later. Second, we discuss the use of stored energy for serving part of the demand at peak load times. This implies increasing the demand load at off-peak times through battery charging and decreasing the demand load at peak load times through discharging. We conclude with some open challenges in demand load management.

Migrate or not? exploiting dynamic task migration in mobile cloud computing systems

Contemporary mobile devices generate heavy loads of computationally intensive tasks, which cannot be executed locally due to the limited processing and energy capabilities of each device. Cloud facilities enable mobile devices-clients to offload their tasks to remote cloud servers, giving birth to Mobile Cloud Computing (MCC). The challenge for the cloud is to minimize the task execution and data transfer time to the user, whose location changes due to mobility. However, providing quality of service guarantees is particularly challenging in the dynamic MCC environment, due to the time-varying bandwidth of the access links, the ever changing available processing capacity at each server and the timevarying data volume of each virtual machine. In this article, we advocate the need for novel cloud architectures and migration mechanisms that effectively bring the computing power of the cloud closer to the mobile user. We consider a cloud computing architecture that consists of a back-end cloud and a local cloud, which is attached to wireless access infrastructure (e.g. LTE base stations). We outline different classes of task migration policies, spanning fully uncoordinated ones, in which each user or server autonomously makes its migration decisions, up to the cloud-wide migration strategy of a cloud provider. We conclude with a discussion of open research problems in the area.