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Mo Sha

Assistant Professor
Department of Computer Science
Binghamton University |
State University of New York

Office: EB Q12
Email: msha AT
Phone: (607) 777-3507

Mailing Address:
4400 Vestal Parkway East, Computer Science,
Binghamton, NY 13902-6000

 Representative Research Work

Industrial Wireless Sensor and Actuator Networks

Process control and automation is the fundamental task in industrial facilities such as oil refineries, chemical plants, and factories. Today's industry mainly relies on wired networks (e.g., HART) to monitor and control their production processes. Cables are used for connecting sensors and forwarding sensor readings to a control room where a controller sends commands to actuators. However, these wired systems suffer from significant drawbacks regarding cost and flexibility. It is very costly to deploy and maintain such wired systems, since thousands of cables have to be installed and maintained, which often requires laying cables underground. This severely complicates effort to reconfigure systems to accommodate new production process requirements. Wireless technology offers a promising platform for process control and automation applications because it does not require any wired infrastructure. Wireless Sensor and Actuator Networks (WSANs) can be used to easily and inexpensively retrofit existing industrial facilities without the need to run dedicated cabling for communication and power. However, industrial WSANs pose unique challenges due to their critical demands on reliable and real-time communication. In this project, we built an experimental testbed by realizing the key networking mechanisms specific to industrial WSANs and then performed a series of empirical studies on WSAN protocols. Based on our insights, we developed several new algorithms and protocols to enable resilient wireless sensor networking in industrial environments.

This work has been reported in IWQoS'14, EWSN'15, RTSS'15, IoTDI'16, PIEEE'16, INFOCOM'17, IWQoS'17, IoT-J'17.

CRII: NeTS: Self-Adaptation in Industrial Wireless Sensor-Actuator Networks


Smart Energy

The largest source of energy consumption in buildings is heating, ventilation, and air conditioning (HVAC). For an HVAC system to provide comfort and minimize energy, it is crucial to understand the spatiotemporal thermal dynamics, especially in a large open public space. To optimize HVAC control, it is important to establish data-driven dynamic models. In this project, we constructed a real-world testbed by instrumenting multiple types of sensors within an HVAC-controlled auditorium to collect real-world data. Based on our dataset, we developed a novel and practical approach to model the complex thermal dynamics in a large space through a combination of data clustering and system identification techniques. We also investigated the impact of different air quality metrics on the HVAC performance.

This work has been reported in ICDCS'14 and Building'15.


Smart Home

Home area networks (HANs) consisting of wireless sensors have emerged as the enabling technology for important applications such as smart energy. These applications impose unique QoS constraints, requiring low data rates but high network reliability in the face of unpredictable wireless environments. In this project, we performed two in-depth empirical studies on wireless channels in real homes, providing key design guidelines for meeting the QoS constraints of HAN applications. The spectrum study analyzes spectrum usage in the 2.4 GHz band where HANs based on the IEEE 802.15.4 standard must coexist with existing wireless devices. We characterized the ambient wireless environment in six apartments through passive spectrum analysis across the entire 2.4 GHz band over seven days in each apartment. We found that the wireless conditions in these residential environments are much more complex and varied than in a typical office environment. Moreover, while 802.11 signals play a significant role in spectrum usage, there also exists non-negligible noise from non-802.11 devices. The multichannel link study measures the reliability of different 802.15.4 channels through active probing with motes in ten apartments. We found that there is not always a persistently reliable channel over 24 hours, and that link reliability does not exhibit cyclic behavior at daily or weekly timescales. Nevertheless, reliability can be maintained through infrequent channel hopping, suggesting dynamic channel hopping as a key tool for meeting the QoS requirements of HAN applications. Our empirical studies provide important guidelines and insights in designing HANs for residential environments.

This work has been reported in IWQoS'11, RTAS'11, and TNSM'13.


Wireless Health

The integration of wireless sensors with mobile phones is gaining momentum as an enabling platform for numerous emerging applications. These mobile systems face dynamic environments where both application requirements and ambient wireless conditions change frequently. Despite the existence of many MAC protocols, none can provide optimal characteristics along multiple dimensions, especially when the conditions are frequently changing. Instead of pursuing a one-MAC-fit-all approach, we developed the Self-Adapting MAC Layer (SAML) that dynamically selects and switches MAC protocols to gain the desired characteristics in response to changes in ambient conditions and application requirements. SAML comprises (1) a Reconfigurable MAC Architecture (RMA) that can switch to different MAC protocols at run time and (2) a learning-based MAC Selection Engine that selects the protocol most suitable for the current condition and requirements. To the application, SAML appears as a traditional MAC layer and realizes its benefits through a simple API for the mobile applications. We implemented SAML in TinyOS 2.x and built three prototypes containing up to five MACs. We validated our system in controlled tests and real-world environments using a new gateway device that integrates a 802.15.4 radio with Android phones.

This work has been reported in RTSS'13 and GLOBECOM'12.


Wireless Sensor Networks Protocols

Low Power Listening (LPL) is a common MAC-layer technique for reducing energy consumption in wireless sensor networks, where nodes periodically wakeup to sample the wireless channel to detect activity. However, LPL is highly susceptible to false wakeups caused by environmental noise being detected as activity on the channel, causing nodes to spuriously wakeup in order to receive nonexistent transmissions. In empirical studies in residential environments, we observed that the false wakeup problem can significantly increase a node's duty cycle, compromising the benefit of LPL. We also found that the energy-level threshold used by the Clear Channel Assessment (CCA) mechanism to detect channel activity has a significant impact on the false wakeup rate. We then developed AEDP, an adaptive energy detection protocol for LPL, which dynamically adjusts a node's CCA threshold to improve network reliability and duty cycle based on application-specified bounds. Empirical experiments in both controlled tests and real-world environments showed AEDP can effectively mitigate the impact of noise on radio duty cycles, while maintaining satisfactory link reliability.

This work has been reported in IPSN'13.


Wireless Sensor Networks Protocols

Transmission power control (TPC) has the potential to reduce power consumption in wireless sensor networks. However, despite a multitude of existing protocols, they still face significant challenges in real-world deployments. A practical TPC protocol must be robust against complex and dynamic wireless properties, and efficient for resource-constrained sensors. In this project, we developed P-TPC, a practical TPC protocol designed on control-theoretic techniques. P-TPC features a highly efficient controller designed on a dynamic model that combines a theoretical link model with online parameter estimation. P-TPC's robustness and energy savings are demonstrated through trace-driven simulations and real-world experiments in a campus building and residential environments.

This work has been reported in ICNP'12.


Wireless Sensor Networks Protocols

Interference modeling is crucial for the performance of numerous wireless protocols such as congestion control, link/channel scheduling, and QoS-aware routing. Understanding and mitigating interference becomes increasingly important for Wireless Sensor Networks (WSNs) as they are being deployed for data-intensive applications such as structural health monitoring. However, previous works have widely adopted simplistic interference models that fail to capture the wireless realities such as probabilistic packet reception performance. Recent studies have suggested that the PRR-SINR model is significantly more accurate than other existing interference models. However, existing PRR-SINR modeling approaches exclusively rely on the use of active measurement packets, which imposes prohibitively high overhead to bandwidth-limited WSNs. In this project, we developed the passive interference measurement (PIM) approach to tackle the complexity of accurate physical interference characterization. PIM is opportunistic in nature as it exploits the spatiotemporal diversity of data traffic for radio performance profiling and only gathers a small amount of information about the network. We evaluated the efficiency of PIM through extensive experiments on both a 13-node a 40-node testbeds of TelosB motes. Our results show that PIM can achieve high accuracy of PRR-SINR modeling with significantly lower overhead compared with an active measurement method.

This work has been reported in ICNP'10.


Wireless Sensor Networks Protocols

Multi-channel design has received significant attention for low-power wireless networks (LWNs), such as 802.15.4-based wireless sensor networks, due to its potential of mitigating interference and improving network capacity. However, recent studies reveal that the number of orthogonal channels available on commodity wireless platforms is small, which significantly hinders the performance of existing multichannel protocols. A promising solution is to explore the use of partially overlapping channels for communications. However, this approach faces several key challenges such as increased inter-channel interference and significantly higher overhead of channel measurement. In this project, we systematically studied the inter-channel interference and its impact on link capacity and the performance of multi-channel protocols in LWNs. First, we developed empirical models for characterizing inter-channel signal attenuation based on experiments on TelosB motes. We then developed a novel algorithm which can significantly reduce the overhead of multi-channel interference measurement by exploiting the spectral power density (SPD) of the transmitter. Finally, we applied our interference models to both link capacity analysis and channel assignment protocols. Our extensive experiments on a testbed of 30 TelosB motes show that our interference measurement algorithm has an average error of 2.95%. Our results also demonstrate that multi-channel protocols for LWNs can significantly benefit from using overlapping channels.

This work has been reported in RTSS'09.


Wireless Sensor Networks Protocols

Recently wireless sensor networks have been deployed for several data-intensive sensing applications such as structural monitoring and habitat monitoring. Sensor nodes in these applications often sample the physical environments at high rates. Supporting data-intensive applications poses several major challenges to the design of WSNs. Due to tight power budget, radios on sensor nodes have very limited bandwidth. In addition, sensor data usually must be delivered to the sink through multiple hops. The achievable delivery rate of a WSN is thus limited by the interference among transmitting nodes. As a result, a fundamental tension exists between the sheer amount of data generated by sensor nodes and the low capacity of WSNs. Moreover, the low throughput of a network also leads to poor energy efficiency as nodes must remain active for a longer period. Although a number of MAC protocols exist for WSNs, they are not designed to achieve high throughput for data-intensive sensing applications. In this project, we developed a new MAC protocol called C-MAC that is designed to achieve high-throughput bulk communication for data-intensive sensing applications. The key novelty of C-MAC is the exploitation of concurrent wireless channel access based on empirical power control and physical interference models. C-MAC has been implemented in TinyOS-1.x and extensively evaluated on Tmote Sky nodes.

This work has been reported in INFOCOM'09.


Network Connectivity

In this project, we studied the critical sensor density for partial connectivity in large area sensor networks. Assuming sensor deployment follows the Poisson distribution, for a given partial connectivity requirement p, 0.5 < p < 1, we proved that there exists a critical sensor density D0, around which the probability that at least 100p% of sensors are inter-connected in the network increases sharply from E to 1- E within a short interval of sensor density D. The length of this interval is in the order of O(1/logA), where A is the area of the sensor field, and the location of D0 is at the sensor density where the above probability is about 1/2. We proved the above theoretical results in the hexagonal lattice model. We also extended our results to the disk model that models transmission range of sensors as disks. Simulation results have verified our theoretical results and exhibited a close match of the results in the lattice model with the disk model.

This work has been reported in INFOCOM'10mini, TOSN'11.