Secure Context-Aware Traffic Light Scheduling System: Integrity of Vehicles’ Identities
Abstract
1. Introduction
2. Related Work
2.1. Context-Aware Traffic Light Scheduling Algorithms
2.2. Secure Traffic Light Scheduling Algorithms
3. The Possible Attacks in the Context-Aware Traffic Light Scheduling Systems and Threat Models
- Vehicles on the same traffic flow as the attacker vehicle can take advantage of the attack. They are scheduled to pass the intersection fast with the fake emergency vehicle announced. Thus, we can call these vehicles innocent beneficiaries (i.e., gainers); they are colored green in Figure 1.
- Vehicles on the other flows will be negatively affected by the attack (i.e., victims). They will have to wait for the traffic flow that contains the fake emergency vehicle to pass through the intersection. These vehicles are colored red in Figure 1.
- Vehicles on the other side of the emergency vehicle can take advantage of the attack. They are scheduled to pass the intersection quickly due to the denial of emergency vehicles. Thus, these vehicles are the gainers of this attack; they are colored green in Figure 2. One of these vehicles may be the initiator of this type of attack.
- Vehicles on the same traffic flow will be negatively affected by the attack (i.e., victims). These vehicles are red in Figure 2.
4. Secure Context-Aware Traffic Light Scheduling System
- Phase 1: Vehicle Registration
- All vehicles on the road network should be registered with a certain traffic authority (TA). This is centrally run by the government. Each vehicle should be assigned a unique identifier to be distinguished and tracked over the road network. Its type (), if it is an ambulance, fire truck, police, or regular vehicle, should also be identified inside the associated database of the TA.
- Generation of cryptographic keys: The traffic authority (TA) produces a distinct ECDSA key pair which includes a public key () and private key () for every registered vehicle, using Cryptographically Secure Pseudorandom Number Generation (CSPRNG) [36].
- Message Digest: The generated message should be hashed using an efficient and secure function such as SHA-2 [34]. This is to create a message digest for that vehicle that could be added to any message and prove the identities of vehicles. This includes the vehicle identification , emergency type , public key , issuing date/time (, and validity period . Equation (1) computes the message digest used to sign messages efficiently.
- Certificate generation: The TA generates a digital certificate () that is encrypted by the private key of that TA () for every vehicle. The is used to be encrypted by the private key of the TA. Equation (2) illustrates how to generate the certificate of each vehicle v inside the TA. The private key is known by the assigned vehicle, and it remains secure within the TA and connected to that .
- Phase 2: Connection Setup
- Phase 3: Transmitting Signed Messages.
- Message Creation: Based on the target and mission of each message, its fields are selected accordingly. For example, periodic “hello” messages contain , , , , etc. Traffic report messages contain the covered area, traffic speed, traffic density, etc. Other data announcing and gathering messages can be created and sent according to the running protocol’s targets and procedures.
- Sign the Message: The ShA-2 hashing algorithm is used to generate a message digest for that message ()). Then, the private key of the sender vehicle () is used to create a message certificate () by encrypting the . The latter certificate ensures that this vehicle initiates that message and other fields of the message have not been manipulated by other users or attackers. Using the hashing algorithm before encrypting the data reduces its size; thus, it reduces the time complexity for the encryption and decryption processes.The value of is added as a signature at the end of the original message. It is mainly used to verify the content of the message and the identity of its initiator. Figure 6 graphically illustrates the steps and techniques of signing a message.
- Phase 4: Signature Verification
- Phase 5: Traffic Light Scheduling
4.1. The Computational and Message Overhead Complexity
4.1.1. Computational Overhead
4.1.2. Message Overhead (Signature Size)
5. Performance Evaluation
5.1. Impersonation Attack
5.2. Modification Attack
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Control Mechanism | Traffic Characteristics Gathering | Technology Used | Main Considerations | Considered Vehicles |
---|---|---|---|---|
Context-aware schedule [2,3] | Periodic advertisement messages | Context-aware algorithms and sensors | Optimize traffic signal timings to reduce traffic congestion and travel time | Emergency vehicles |
Context-aware negotiation [23] | Real-time traffic data | VANETs, and sensors | Reduce the congestion at signalized intersections | Regular vehicles |
SmartLight [4] | Collect traffic characteristics | VANETs, and sensors | Reduce fuel consumption and waiting time of vehicles | Heavy vehicles |
Efficient Adaptive Control System [24] | Detect the presence of vehicles | Magnetometer sensor | Reduce traffic congestion and improve traffic flow efficiency | Regular vehicles |
Secure Mechanism | Security Tool | Technology Used | Security Considerations | Vehicle Considerations |
---|---|---|---|---|
Secure intelligent traffic light [27] | Diffie–Hellman (CDH) algorithms, and puzzle | Fog computing | Prevent DoS attacks | Only regular vehicles |
A fog-based security framework [28] | Digital signatures | Fog computing | Prevent replay, DoS, Sybil, and impersonation attacks | Only regular vehicles |
Intelligent traffic management system [29] | Digital and anonymous certificates | VANETs and Internet of Vehicles (IoV) | Ensuring that vehicles are communicating with real traffic signals | Only regular vehicles |
Secure context aware [30] | Digital signature certificates | Sensors and VANETs | Prevent message alteration, message replay attack, and identity impersonation of a vehicle | Only regular vehicles |
The Threat | Initiator | Definition | Beneficiaries | Affected |
---|---|---|---|---|
Impersonation Attack | Regular Vehicle (Attacker) | A regular vehicle (attacker) pretends to be an emergency vehicle | The attacker vehicle and all vehicles located on the same traffic flow (Less waiting delay time) | All vehicles located on the other competing traffic flows (More waiting delay time) |
Packet Manipulation Attack | External Intruder | An external intruder manipulates the packet sent by the emergency vehicle to make it look like a regular vehicle | All vehicles located on the traffic flows that are competing with the traffic flow that originally contains the emergency vehicle (Less waiting delay time) | The emergency vehicle and all the vehicles located on the same traffic flow (More waiting delay time) |
Parameter | Value |
---|---|
Simulator | NS-2.35, SUMO 1.24.0 |
Transmission range (m) | 200 |
No. of traffic lights | 1 |
No. of emergency vehicles | 1 |
Simulation area () | 1000 m × 1000 m |
Number of vehicles | 200, 400, 600, 800, 1000 |
Simulation time | 10,000 |
The map | 4 legs intersection |
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Share and Cite
Yahia, M.; Bani Younes, M.; Najjar, F.; Audat, A.; Ghoul, S. Secure Context-Aware Traffic Light Scheduling System: Integrity of Vehicles’ Identities. World Electr. Veh. J. 2025, 16, 448. https://doi.org/10.3390/wevj16080448
Yahia M, Bani Younes M, Najjar F, Audat A, Ghoul S. Secure Context-Aware Traffic Light Scheduling System: Integrity of Vehicles’ Identities. World Electric Vehicle Journal. 2025; 16(8):448. https://doi.org/10.3390/wevj16080448
Chicago/Turabian StyleYahia, Marah, Maram Bani Younes, Firas Najjar, Ahmad Audat, and Said Ghoul. 2025. "Secure Context-Aware Traffic Light Scheduling System: Integrity of Vehicles’ Identities" World Electric Vehicle Journal 16, no. 8: 448. https://doi.org/10.3390/wevj16080448
APA StyleYahia, M., Bani Younes, M., Najjar, F., Audat, A., & Ghoul, S. (2025). Secure Context-Aware Traffic Light Scheduling System: Integrity of Vehicles’ Identities. World Electric Vehicle Journal, 16(8), 448. https://doi.org/10.3390/wevj16080448