Rising global trade is fuelling logistics expansion, but there are growing pains
Beyond current short-term economic headwinds, World Economic Forum projects global trade volume to grow 4 times by 2050, with a value of over $68tn globally.
More goods transported (domestically and internationally) over large distances would lead to multi-fold growth in cargo movement. As per European Road Transport Research Advisory council, this would boost freight’s share in traffic on urban road networks, estimated 15% in EU by 2050.
Traffic congestions would mean delayed shipments, thus increasing cost of transport, greater carbon emissions and lower value throughput from supply chains. US Department of Transportation pegs this impact in escalating operation costs and fuel wastage is estimated around $28mn each year, just for US logistics industry.
Developing economies are becoming growth hotspot
Developing economies in Africa and Asia have a large share in trade growth – both domestic and international.
Supply chain investment priorities are shifting
Historically, trade growth projections boosted corporate investments in capacity expansion in fleet size, warehouse space, and distribution operations. However, there is an emerging investment trend in digitalizing supply chain. These are seen across hardware (sensors, gateway devices, and firmware) as well as software (IoT, cloud, ML, and analytics) spectrum. Over the next decade, business value from autonomous trucks (US$30b) and drones (US$20b) is substantial.
Digitizing supply chains will help mitigate key business challenges
- Fleet monitoring and geo-fencing of trucks would ensure optimized routing of cargo through dynamic, unpredictable roads, and traffic conditions.
- Monitoring, analysing, and controlling on-board ambient conditions like temperature, humidity and airflow will help preserve perishable and high value cargo.
- Analysing driver behaviour, in cabin as well as on-road, through audio and video analytics will enhance safety, predict and minimize risks through focused driver training or automated driver assistance.
Addressing new operational challenges
While we have addressed key business challenges, there can also be operational challenges that need to be addressed that will help in securing and improving connected logistics.
Simultaneous firmware + RTOS updates, real time device provisioning, and authentication across edge node population requires a robust remote device management system. Diverse sensors on commercial edge devices use multiple communication protocols like BLE, ZigBee, Wi-fi etc. to send data feeds that gateways need to process.
Most important, though, is an edge security mechanism, which provisions edge sensors securely, reliably, and gathers high fidelity data feeds, particularly for critical edge workloads like fleet tracking/ geo fencing and ADAS.
Security is the glue that holds IoT value chain together
The transportation and logistics being one of the most targeted industries by cyber attackers, potential value erosion is also sizeable. Securing these complex cyber-physical systems involves multi-layer security from sensor to cloud.
- Edge sensors for parameters like vehicle health (fuel levels, tyre pressure, engine temperature), cargo ambient conditions (temperature, humidity) as well as driver behaviour need to be securely provisioned, data feeds acquired and communicated to cloud.
- Lack of securely acquired, high fidelity inputs i.e. tampered, intermittent, and generally unreliable sensor feeds increases risk of vehicle downtime, instances of undesired behaviour by driver and cargo condition deterioration.
- Gateways serve as communication hubs and conversely present a single point of failure for a set of connected devices.
- A security compromised gateway manifests in erroneous processing of multi-protocol communication sensor feeds, failed multicast communication leading to inaccurate Edge intelligence based analytics and device control actions not taken.
- Cloud data management platforms need to be equipped with appropriate level of security w.r.t multi-tenancy, partitioning, and encryption so that diagnostic and predictive analytics generated on device data feeds are accurate and generate actionable insights.
- User applications like data visualization and self-service analytics need robust security mechanism with secure user access management practices, feature level privileges so that data access is role appropriate.
Securing the weak link of IoT value chain – the Edge
Most organizations running IoT workloads rely on public cloud services to run their IoT data management workloads and end user analytical applications. Economies of business scale permit leading public cloud providers invest in state-of-the-art security infrastructure as well as extensive security policy frameworks, thus sufficiently securing the cloud part of IoT stack.
Edge security, though, is more complex, locally managed and thus contributes most to IoT security risks. Some ways organizations can secure their edge infrastructures for running IoT workloads are
- Public key based device certificates like X.509 so that sensors get discovered, authenticated, registered, and provisioned securely.
- Encrypting sensor state data in motion (feeds from edge to gateway, aggregated data pipe from gateway to cloud) as well as data at rest (device metadata store, edge intelligence rule repository) so that data is processed and device state threshold based actions are initiated at the edge appropriately.
- API and micro-services based 3rd party data/ application integration at edge and gateway layer so that context related enrichment of sensor data feeds yields more real time relevant and highly actionable insights.
eInfochips has significant IoT expertise and experience in building secure industry focused IoT applications, including some deployments in logistics and transportation sector as well – from sensor to cloud. To know more click here.