Wireless sensor networks have become increasingly common for monitoring various environmental factors and collecting data over remote areas. Ensuring a wireless sensor network is performing as intended and can reliably transmit sensor data is important. Here are some methods students can use to evaluate the performance of a wireless sensor network and identify any potential issues:

Connectivity Testing – One of the most basic but important tests students can do is check the connectivity and signal strength between sensor nodes and the data collection point, usually a wireless router. They should physically move around the sensor deployment area with a laptop or mobile device to check the signal strength indicator from each node. Any nodes showing weak or intermittent signals may need to have their location adjusted or an additional node added as a repeater to improve the mesh network. Checking the signal paths helps identify areas that may drop out of range over time.

Packet Loss Testing – Students should program the sensor nodes to transmit test data packets on a frequent scheduled basis. The data collection point can then track if any packets are missing over time. Consistent or increasing packet loss indicates the wireless channels may be too congested or experiencing interference. Environmental factors like weather could also impact wireless signals. Noteing times of higher packet loss can help troubleshoot the root cause. Replacing older battery-powered nodes prevent dropped signals due to low battery levels.

Latency Measurements – In addition to checking if data is lost, students need to analyze the latency or delays in data transmission. They can timestamp packets at the node level and again on receipt to calculate transmission times. Consistently high latency above an acceptable threshold may mean the network cannot support time-critical applications. Potential causes could include low throughput channels, network congestion between hops, or too many repeating nodes increasing delays. Latency testing helps identify bottlenecks needing optimization.

Throughput Analysis – The overall data throughput of the wireless sensor network is important to measure against the demands of the IoT/sensor applications. Students should record the throughput over time as seen by the data collection system. Peaks in network usage may cause temporary drops, so averaging is needed. Persistent low throughput under the expectations indicates insufficient network capacity. Throughput can decrease further with distance between nodes, so additional nodes may be a solution. Too many nodes also increases the medium access delays.

Node Battery Testing – As many wireless sensor networks rely on battery power, students must monitor individual node battery voltages over time to catch any draining prematurely. Low batteries impact the ability to transmit sensor data and can reduce the reliability of that node. Replacing batteries too often drives up maintenance costs. Understanding actual versus expected battery life helps optimize the hardware, duty cycling of nodes, and replacement schedules. It also prevents complete loss of sensor data collection from nodes dying.

Hardware Monitoring – Checking for firmware or software issues requires students to monitor basic node hardware health indicators like CPU and memory usage. Consistently high usage levels could mean inefficient code or tasks are overloading the MCU’s abilities. Overheating sensor nodes is also an indication they may not be properly ventilated or protected from environmental factors. Hardware issues tend to get worse over time and should be addressed before triggering reliability problems on the network level.

Network Mapping – Students can use network analyzer software tools to map the wireless connectivity between each node and generate a visual representation of the network topology. This helps identify weak points, redundant connections, and opportunities to optimize the routing paths. It also uncovers any nodes that aren’t properly integrating into the mesh routing protocol which causes blackholes in data collection. Network mapping makes issues easier to spot compared to raw data alone.

Conduction interference testing involves using additional wireless devices within range of sensor nodes to simulate potential sources of noise. Microwave ovens, baby monitors, WiFi routers and other 2.4GHz devices are common culprits. By monitoring the impact on connectivity and throughput, students gain insights on how robust the network is against real-world coexistence challenges. It also helps determine requirements like transmit power levels needed.

Regular sensor network performance reviews are important for detecting degrading reliability before it causes major issues or data losses. By methodically evaluating common metrics like those outlined above, students can thoroughly check the operation of their wireless infrastructure and identify root causes of any anomalies. Taking a proactive approach to maintenance through continuous monitoring prevents more costly troubleshooting of severe and widespread failures down the road. It also ensures the long-term sustainability of collecting important sensor information over time.