S. M. Iftekharul Alam

A practical approach for provenance transmission in wireless sensor networks

Assessing the trustworthiness of sensor data and transmitters of this data is critical for quality assurance. Trust evaluation frameworks utilize data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, resulting in high energy dissipation due to the extended period of radio communication. In this paper, we design energy-efficient provenance encoding and construction schemes, which we refer to as Probabilistic Provenance Flow (PPF). Our work demonstrates the feasibility of adapting the Probabilistic Packet Marking (PPM) technique in IP traceback to wireless sensor networks. We design two bit-efficient provenance encoding schemes along with a complementary vanilla scheme. Depending on the network size and bit budget, we select the best method based on mathematical approximations and numerical analysis. We integrate PPF with provenance-based trust frameworks and investigate the trade-off between trustworthiness of data items and transmission overhead. We conduct TOSSIM simulations with realistic wireless links, and perform testbed experiments on 15-20TelosB motes to demonstrate the effectiveness of PPF. Our results show that the encoding schemes of PPF have identical performance with a low bit budget (~32-bit), requiring 33% fewer packets and 30% less energy than PPM variants to construct provenance. With a twofold increase in bit budget, PPF with the selected encoding scheme reduces energy consumption by 46-60%.

SYREN: Synergistic Link Correlation-Aware and Network Coding-Based Dissemination in Wireless Sensor Networks

Rapid flooding is necessary for code updates and routing tree formation in wireless sensor networks. Link correlation-aware collective flooding (CF) is a recently proposed technique that provides a substrate for efficiently disseminating a single packet. Applying CF to multiple packet dissemination poses several challenges, such as reliability degradation, redundant transmissions, and increased contention among node transmissions. The varying link correlation observed in real networks makes the problem harder. In this paper, we propose a multi-packet flooding protocol, SYREN, that exploits the synergy among link correlation and network coding. In particular, SYREN exploits link correlation to eliminate the overhead of explicit control packets in networks with high correlation, and uses network coding to pipeline transmission of multiple packets via a novel, single yet scalable timer per node. SYREN reduces the number of redundant transmissions while achieving near-perfect reliability, especially in networks with low link correlation. Test bed experiments and simulations show that SYREN reduces the average number of transmissions by 30% and dissemination delay by more than 60% while achieving the same reliability as state-of-the-art protocols.

Multi-armed Bandit Congestion Control in Multi-hop Infrastructure Wireless Mesh Networks

Congestion control in multi-hop infrastructure wireless mesh networks is both an important and a unique problem. It is unique because it has two prominent causes of failed transmissions which are difficult to tease apart - lossy nature of wireless medium and high extent of congestion around gateways in the network. The concurrent presence of these two causes limits applicability of already available congestion control mechanisms, proposed for wireless networks. Prior mechanisms mainly focus on the former cause, ignoring the latter one. Therefore, we address this issue to design an end-to-end congestion control mechanism for infrastructure wireless mesh networks in this paper. We formulate the congestion control problem and map that to the restless multi-armed bandit problem, a well-known decision problem in the literature. Then, we propose three myopic policies to achieve a near-optimal solution for the mapped problem since no optimal solution is known to this problem. We perform comparative evaluation through ns-2 simulation and a real testbed experiment with a wireline TCP variant and a wireless TCP protocol. The evaluation reveals that our proposed mechanism can achieve up to 52% increased network throughput and 34% decreased average energy consumption per transmitted bit in comparison to the other end-to-end congestion control variants.

Link correlation and network coding in broadcast protocols for wireless sensor networks

Correlated packet reception can be advantageous for sensor network broadcast protocols. By exploiting link correlation information, researchers have devised efficient single packet flooding protocols. In this work, we use testbed experiments to gain insight into the behavior of link correlation-aware broadcast protocols. We observe that, in the presence of varying link correlation, traditional link correlation-aware flooding mechanisms do not perform well in disseminating multiple packets due to reliability requirements and redundant transmissions. We conduct simulations to compare existing link correlation-aware flooding protocols with two versions of a multi-packet dissemination protocol, where one uses network coding and the other exploits both link correlation and network coding. Simulation results indicate the potential of the latter approach to be used as a reliable multi-packet dissemination protocol in practical scenarios. We also compare this protocol with existing multi-packet dissemination protocols, and reveal cases when certain protocols perform better than others.

LiTMaS: Live road traffic maps for smartphones

A smartphone application that displays a live view of road traffic can provide drivers with real-time information on traffic jams, flash floods or accidents along their planned routes. This information aids them in avoiding delays and uncertainties on the road, enhancing their navigation experience. Several US departments of transportation provide information from street cameras in the form of a snapshot of a particular location on a Google map. However, it is difficult for drivers to manually select cameras on the map to obtain consolidated information about road conditions along a particular route. In this paper, we design an energy-efficient mobile service that provides drivers with a convenient interface to observe live camera coverage along a route. Our proposed solution comprises an Android application and a cloud-based proxy service between smartphones and traffic cameras. The proxy provides an abstraction layer over different communication protocols adopted by traffic cameras, and transfers camera images for a specified route to the smartphone as a batch, thereby reducing communication overhead and improving user response time. By employing an in-memory cache, the proxy server maintains the most recently accessed camera images and reduces camera polling. We integrate our solution with traffic cameras from cities in Massachusetts, New York, and Washington DC, and demonstrate its reduced energy consumption and reduced response time.

An energy-efficient approach for provenance transmission in wireless sensor networks

Assessing the trustworthiness of sensor data and transmitters of this data is critical for quality assurance. Trust evaluation frameworks utilize data provenance along with the sensed data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop sensor network, provenance information requires a large and variable number of bits in each packet, resulting in high energy dissipation due to the extended period of radio communication, and making trust systems unusable. We propose energy-efficient provenance encoding and construction schemes, which we refer to as Probabilistic Provenance Flow (PPF). To the best of our knowledge, ours is the first work to make the Probabilistic Packet Marking (PPM) approach for IP traceback feasible for sensor networks. We design two bit-efficient provenance encoding schemes along with a complementary vanilla scheme. Depending on the network size and bit budget, we select the best method using mathematical approximations and numerical analysis. Our TOSSIM simulations demonstrate that the encoding schemes of PPF have identical performance with a low bit budget (~ 32-bit), requiring 33% fewer packets and 30% less energy than PPM variants to construct provenance. With a two-fold increase in bit budget, PPF with the selected encoding scheme reduces the energy consumption by 60%1.

ERUPT: Energy-efficient trustworthy provenance trees for wireless sensor networks

Sensor nodes are inherently unreliable and prone to hardware or software faults. Thus, they may report untrustworthy or inconsistent data. Assessing the trustworthiness of sensor data items can allow reliable sensing or monitoring of physical phenomena. A provenance-based trust framework can evaluate the trustworthiness of data items and sensor nodes based on the intuition that two data items with similar data values but with different provenance (i.e., forwarding path) can be considered more trustworthy. Forwarding paths of data items generated from redundantly deployed sensors should consist of trustworthy nodes and remain dissimilar. Unfortunately, operating many sensors with dissimilar paths consumes significant energy. In this paper, we formulate an optimization problem to identify a set of sensor nodes and their corresponding paths toward the base station that achieve a certain trustworthiness threshold, while keeping the energy consumption of the network minimal. We prove the NP-hardness of this problem and propose ERUPT, a simulated annealing solution. Testbed and simulation results show that ERUPT achieves high trustworthiness, while reducing total energy consumption by 32-50% with respect to current approaches.

Feluda: Provenance-Enabled Diagnosis of Elusive Network Failures in Wireless Sensor Networks

Sensor nodes are prone to failures due to their limited hardware capabilities, and software uncertainties stemming from erroneous logic or configuration. Such failures as well as wireless channel dynamics can degrade network performance, potentially creating network partitions. Existing troubleshooting tools either only diagnose a few problems or suffer from high overhead due to periodic transmission of control packets. In this paper, we propose Feluda, a system that exploits provenance, i.e., forwarding path of data packets, for automatic localization of problematic nodes and packets. Unlike existing methods, Feluda extracts necessary network performance metrics from packet headers and stores them into node flash storage, thereby reducing out-of-band packet transmissions. Once problematic nodes and corresponding packets are identified at the base station (BS), Feluda provides efficient querying mechanisms to retrieve packet headers of interest from specific nodes. Packet header analysis reveals the root cause of the problem. We implement Feluda using Java and ContikiOS on the BS and sensor nodes, respectively. Testbed experiments and COOJA simulations demonstrate the effectiveness of Feluda compared to the state-of the-art.